Overview

Dataset statistics

Number of variables217
Number of observations60537
Missing cells1416862
Missing cells (%)10.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory224.9 MiB
Average record size in memory3.8 KiB

Variable types

Categorical193
Numeric24

Alerts

STATEFP has constant value "17.0" Constant
MTFCC has constant value "G5020" Constant
FUNCSTAT has constant value "S" Constant
CFTYPE has constant value "Park" Constant
CFSUBTYPE has constant value "Park" Constant
casenumber has a high cardinality: 60537 distinct values High cardinality
incident_date_x has a high cardinality: 52231 distinct values High cardinality
death_date_x has a high cardinality: 2699 distinct values High cardinality
residence_city has a high cardinality: 1124 distinct values High cardinality
primarycause has a high cardinality: 8798 distinct values High cardinality
secondarycause has a high cardinality: 9266 distinct values High cardinality
primarycause_linea has a high cardinality: 2526 distinct values High cardinality
primarycause_lineb has a high cardinality: 377 distinct values High cardinality
geocoded_address has a high cardinality: 5226 distinct values High cardinality
full_address has a high cardinality: 6311 distinct values High cardinality
NAMELSAD has a high cardinality: 1937 distinct values High cardinality
CFNAME has a high cardinality: 77 distinct values High cardinality
ADDRESS has a high cardinality: 73 distinct values High cardinality
landuse_name has a high cardinality: 54 distinct values High cardinality
death_datetime has a high cardinality: 59410 distinct values High cardinality
death_time has a high cardinality: 1440 distinct values High cardinality
primary_combined has a high cardinality: 12238 distinct values High cardinality
incident_date_y has a high cardinality: 52186 distinct values High cardinality
death_date_y has a high cardinality: 59404 distinct values High cardinality
death_street has a high cardinality: 39810 distinct values High cardinality
death_city has a high cardinality: 293 distinct values High cardinality
death_zip has a high cardinality: 497 distinct values High cardinality
death_location has a high cardinality: 5808 distinct values High cardinality
death_location_1 has a high cardinality: 152 distinct values High cardinality
record_id has a high cardinality: 26350 distinct values High cardinality
cold_related is highly correlated with cold_combinedHigh correlation
heat_related is highly correlated with hot_combinedHigh correlation
opioids is highly correlated with covid_primaryHigh correlation
recovered is highly correlated with closest_pharmacy and 2 other fieldsHigh correlation
final_latitude is highly correlated with INTPTLATHigh correlation
final_longitude is highly correlated with INTPTLON and 1 other fieldsHigh correlation
closest_pharmacy is highly correlated with recovered and 2 other fieldsHigh correlation
FIRST_COUNTY is highly correlated with COUNTYFPHigh correlation
COUNTYFP is highly correlated with FIRST_COUNTY and 1 other fieldsHigh correlation
TRACTCE is highly correlated with GEOID and 2 other fieldsHigh correlation
GEOID is highly correlated with TRACTCE and 2 other fieldsHigh correlation
NAME is highly correlated with TRACTCE and 2 other fieldsHigh correlation
ALAND is highly correlated with TRACTCE and 2 other fieldsHigh correlation
INTPTLAT is highly correlated with final_latitudeHigh correlation
INTPTLON is highly correlated with final_longitude and 1 other fieldsHigh correlation
OBJECTID_12 is highly correlated with final_longitude and 1 other fieldsHigh correlation
death_month is highly correlated with death_weekHigh correlation
death_week is highly correlated with death_monthHigh correlation
hot_combined is highly correlated with heat_relatedHigh correlation
cold_combined is highly correlated with cold_relatedHigh correlation
repeated_address is highly correlated with recovered and 2 other fieldsHigh correlation
repeated_lat_long is highly correlated with recovered and 3 other fieldsHigh correlation
covid_primary is highly correlated with opioids and 133 other fieldsHigh correlation
covid_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxic-ischemic_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxia_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
op-name_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
drug_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
nonfentanyl_opioid_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
opiate_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alcohol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
eth_alc_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxic-ischemic_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxia_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
stimulant_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methamphetamine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_based_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hallucinogen_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzodiazepine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzo_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
sedative_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cocaine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alcohol_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
drug_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
eth_alc_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl-name_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lorazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
clonazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methadone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
anpp_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fen_analog_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
heroin_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
xylazine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
morphine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alprazolam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tramadol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
pcp_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
buprenorphine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydromorphone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydrocodone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
inhalant_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
mitragynine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxycodone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cocaine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
stimulant_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
dihydrocodeine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
diazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
topiramate_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cyclobenzaprine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
toxic_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
temazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
carfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
ketamine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxymorphone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
codeine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methocarbamol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
chlordiazepoxide_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylsalicylic acid_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylsalicylic acid_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
polysubstance_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levomethorphan_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levorphanol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
butyryl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
heroin_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
nonfentanyl_opioid_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
opiate_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl-name_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
anpp_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fen_analog_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
valerylfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
estazolam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
carisoprodol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
inhalant_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lsd_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
propoxyphene_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fbf_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
barbiturates_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_based_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hallucinogen_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
marijuana_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cannabis_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
op-name_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzodiazepine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzo_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
sedative_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methylphenidate_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
u-47700_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
norfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
pcp_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
polysubstance_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
metaxalone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tarpentadol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methadone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cyclopropyl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
phentermine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methoxyacetyl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
furanyl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fbf_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fibf_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
flurazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylfentanyl_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
u-49900_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tizanidine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
furanyl_fentanyl_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lsd_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methamphetamine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydromorphone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydrocodone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
dihydrocodeine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
meperidine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
clonazepam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alprazolam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
xylazine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
mitragynine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
codeine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
ketamine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
diazepam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxycodone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levomethorphan_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levorphanol_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lorazepam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
marijuana_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cannabis_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxymorphone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tramadol_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cyclobenzaprine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
toxic_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
topiramate_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
morphine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
buprenorphine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cold_related is highly correlated with cold_combinedHigh correlation
heat_related is highly correlated with hot_combinedHigh correlation
commissioner_district is highly correlated with final_longitude and 1 other fieldsHigh correlation
opioids is highly correlated with covid_primaryHigh correlation
recovered is highly correlated with closest_pharmacy and 2 other fieldsHigh correlation
final_latitude is highly correlated with INTPTLATHigh correlation
final_longitude is highly correlated with commissioner_district and 1 other fieldsHigh correlation
closest_pharmacy is highly correlated with recovered and 2 other fieldsHigh correlation
FIRST_COUNTY is highly correlated with COUNTYFP and 1 other fieldsHigh correlation
COUNTYFP is highly correlated with FIRST_COUNTY and 1 other fieldsHigh correlation
TRACTCE is highly correlated with NAMEHigh correlation
GEOID is highly correlated with FIRST_COUNTY and 1 other fieldsHigh correlation
NAME is highly correlated with TRACTCEHigh correlation
INTPTLAT is highly correlated with final_latitudeHigh correlation
INTPTLON is highly correlated with commissioner_district and 1 other fieldsHigh correlation
death_month is highly correlated with death_weekHigh correlation
death_week is highly correlated with death_monthHigh correlation
hot_combined is highly correlated with heat_relatedHigh correlation
cold_combined is highly correlated with cold_relatedHigh correlation
repeated_address is highly correlated with recovered and 2 other fieldsHigh correlation
repeated_lat_long is highly correlated with recovered and 2 other fieldsHigh correlation
covid_primary is highly correlated with opioids and 133 other fieldsHigh correlation
covid_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxic-ischemic_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxia_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
op-name_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
drug_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
nonfentanyl_opioid_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
opiate_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alcohol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
eth_alc_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxic-ischemic_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxia_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
stimulant_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methamphetamine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_based_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hallucinogen_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzodiazepine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzo_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
sedative_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cocaine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alcohol_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
drug_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
eth_alc_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl-name_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lorazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
clonazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methadone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
anpp_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fen_analog_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
heroin_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
xylazine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
morphine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alprazolam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tramadol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
pcp_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
buprenorphine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydromorphone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydrocodone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
inhalant_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
mitragynine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxycodone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cocaine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
stimulant_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
dihydrocodeine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
diazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
topiramate_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cyclobenzaprine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
toxic_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
temazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
carfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
ketamine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxymorphone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
codeine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methocarbamol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
chlordiazepoxide_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylsalicylic acid_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylsalicylic acid_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
polysubstance_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levomethorphan_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levorphanol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
butyryl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
heroin_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
nonfentanyl_opioid_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
opiate_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl-name_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
anpp_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fen_analog_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
valerylfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
estazolam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
carisoprodol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
inhalant_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lsd_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
propoxyphene_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fbf_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
barbiturates_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_based_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hallucinogen_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
marijuana_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cannabis_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
op-name_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzodiazepine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzo_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
sedative_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methylphenidate_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
u-47700_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
norfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
pcp_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
polysubstance_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
metaxalone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tarpentadol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methadone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cyclopropyl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
phentermine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methoxyacetyl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
furanyl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fbf_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fibf_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
flurazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylfentanyl_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
u-49900_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tizanidine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
furanyl_fentanyl_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lsd_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methamphetamine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydromorphone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydrocodone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
dihydrocodeine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
meperidine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
clonazepam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alprazolam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
xylazine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
mitragynine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
codeine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
ketamine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
diazepam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxycodone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levomethorphan_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levorphanol_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lorazepam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
marijuana_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cannabis_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxymorphone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tramadol_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cyclobenzaprine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
toxic_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
topiramate_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
morphine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
buprenorphine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cold_related is highly correlated with cold_combinedHigh correlation
heat_related is highly correlated with hot_combinedHigh correlation
opioids is highly correlated with covid_primaryHigh correlation
recovered is highly correlated with closest_pharmacy and 2 other fieldsHigh correlation
final_latitude is highly correlated with INTPTLATHigh correlation
final_longitude is highly correlated with INTPTLONHigh correlation
closest_pharmacy is highly correlated with recovered and 2 other fieldsHigh correlation
FIRST_COUNTY is highly correlated with COUNTYFPHigh correlation
COUNTYFP is highly correlated with FIRST_COUNTY and 1 other fieldsHigh correlation
TRACTCE is highly correlated with GEOID and 1 other fieldsHigh correlation
GEOID is highly correlated with TRACTCE and 1 other fieldsHigh correlation
NAME is highly correlated with TRACTCE and 1 other fieldsHigh correlation
INTPTLAT is highly correlated with final_latitudeHigh correlation
INTPTLON is highly correlated with final_longitudeHigh correlation
death_month is highly correlated with death_weekHigh correlation
death_week is highly correlated with death_monthHigh correlation
hot_combined is highly correlated with heat_relatedHigh correlation
cold_combined is highly correlated with cold_relatedHigh correlation
repeated_address is highly correlated with recovered and 2 other fieldsHigh correlation
repeated_lat_long is highly correlated with recovered and 3 other fieldsHigh correlation
covid_primary is highly correlated with opioids and 133 other fieldsHigh correlation
covid_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxic-ischemic_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxia_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
op-name_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
drug_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
nonfentanyl_opioid_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
opiate_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alcohol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
eth_alc_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxic-ischemic_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hypoxia_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
stimulant_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methamphetamine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_based_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hallucinogen_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzodiazepine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzo_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
sedative_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cocaine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alcohol_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
drug_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
eth_alc_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl-name_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lorazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
clonazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methadone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
anpp_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fen_analog_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
heroin_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
xylazine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
morphine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alprazolam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tramadol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
pcp_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
buprenorphine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydromorphone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydrocodone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
inhalant_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
mitragynine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxycodone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cocaine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
stimulant_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
dihydrocodeine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
diazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
topiramate_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cyclobenzaprine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
toxic_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
temazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
carfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
ketamine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxymorphone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
codeine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methocarbamol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
chlordiazepoxide_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylsalicylic acid_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylsalicylic acid_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
polysubstance_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levomethorphan_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levorphanol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
butyryl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
heroin_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
nonfentanyl_opioid_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
opiate_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl-name_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fentanyl_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
anpp_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fen_analog_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
valerylfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
estazolam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
carisoprodol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
inhalant_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lsd_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
propoxyphene_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fbf_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
barbiturates_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_based_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hallucinogen_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
marijuana_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cannabis_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
op-name_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzodiazepine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
benzo_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
sedative_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methylphenidate_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
u-47700_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
norfentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
pcp_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
polysubstance_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
metaxalone_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tarpentadol_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methadone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cyclopropyl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
phentermine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methoxyacetyl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
furanyl_fentanyl_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fbf_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
fibf_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
flurazepam_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
acetylfentanyl_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
u-49900_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tizanidine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
furanyl_fentanyl_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lsd_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
methamphetamine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydromorphone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
hydrocodone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
dihydrocodeine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
amphetamine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
meperidine_primary is highly correlated with covid_primary and 132 other fieldsHigh correlation
clonazepam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
alprazolam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
xylazine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
mitragynine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
codeine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
ketamine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
diazepam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxycodone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levomethorphan_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
levorphanol_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
lorazepam_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
marijuana_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cannabis_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
oxymorphone_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
tramadol_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
cyclobenzaprine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
toxic_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
topiramate_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
morphine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
buprenorphine_secondary is highly correlated with covid_primary and 132 other fieldsHigh correlation
age is highly correlated with manner and 5 other fieldsHigh correlation
gender is highly correlated with CFNAME and 3 other fieldsHigh correlation
race is highly correlated with CFNAME and 2 other fieldsHigh correlation
latino is highly correlated with FAC_NAMEHigh correlation
cold_related is highly correlated with FAC_NAME and 1 other fieldsHigh correlation
heat_related is highly correlated with hot_combinedHigh correlation
commissioner_district is highly correlated with final_latitude and 15 other fieldsHigh correlation
manner is highly correlated with age and 138 other fieldsHigh correlation
gunrelated is highly correlated with age and 4 other fieldsHigh correlation
opioids is highly correlated with CFNAME and 6 other fieldsHigh correlation
primarycause_linec is highly correlated with landuse_name and 4 other fieldsHigh correlation
geocoded_score is highly correlated with FAC_NAMEHigh correlation
recovered is highly correlated with closest_pharmacy and 7 other fieldsHigh correlation
final_latitude is highly correlated with commissioner_district and 15 other fieldsHigh correlation
final_longitude is highly correlated with commissioner_district and 12 other fieldsHigh correlation
closest_pharmacy is highly correlated with recovered and 7 other fieldsHigh correlation
FIRST_COUNTY is highly correlated with recovered and 8 other fieldsHigh correlation
LANDUSE is highly correlated with OS_MGMT and 7 other fieldsHigh correlation
LANDUSE2 is highly correlated with MODIFIERHigh correlation
OS_MGMT is highly correlated with commissioner_district and 8 other fieldsHigh correlation
FAC_NAME is highly correlated with age and 25 other fieldsHigh correlation
PLATTED is highly correlated with LANDUSE and 3 other fieldsHigh correlation
MODIFIER is highly correlated with LANDUSE2 and 1 other fieldsHigh correlation
COUNTYFP is highly correlated with recovered and 13 other fieldsHigh correlation
TRACTCE is highly correlated with commissioner_district and 13 other fieldsHigh correlation
GEOID is highly correlated with recovered and 13 other fieldsHigh correlation
NAME is highly correlated with commissioner_district and 13 other fieldsHigh correlation
ALAND is highly correlated with final_latitude and 6 other fieldsHigh correlation
AWATER is highly correlated with final_latitude and 4 other fieldsHigh correlation
INTPTLAT is highly correlated with commissioner_district and 14 other fieldsHigh correlation
INTPTLON is highly correlated with commissioner_district and 13 other fieldsHigh correlation
OBJECTID_12 is highly correlated with commissioner_district and 13 other fieldsHigh correlation
CFNAME is highly correlated with age and 149 other fieldsHigh correlation
ADDRESS is highly correlated with gender and 139 other fieldsHigh correlation
GNISCODE is highly correlated with commissioner_district and 8 other fieldsHigh correlation
COMMENT is highly correlated with commissioner_district and 5 other fieldsHigh correlation
SOURCE is highly correlated with commissioner_district and 13 other fieldsHigh correlation
Jurisdicti is highly correlated with age and 18 other fieldsHigh correlation
Community is highly correlated with age and 18 other fieldsHigh correlation
landuse_name is highly correlated with primarycause_linec and 16 other fieldsHigh correlation
landuse_sub_name is highly correlated with LANDUSE and 11 other fieldsHigh correlation
landuse_major_name is highly correlated with LANDUSE and 7 other fieldsHigh correlation
death_month is highly correlated with death_weekHigh correlation
death_week is highly correlated with death_monthHigh correlation
motel is highly correlated with landuse_nameHigh correlation
hot_combined is highly correlated with heat_relatedHigh correlation
cold_combined is highly correlated with cold_related and 1 other fieldsHigh correlation
matching_addresses is highly correlated with primarycause_linecHigh correlation
repeated_address is highly correlated with recovered and 5 other fieldsHigh correlation
repeated_lat_long is highly correlated with recovered and 5 other fieldsHigh correlation
death_county is highly correlated with death_stateHigh correlation
death_state is highly correlated with death_countyHigh correlation
covid_primary is highly correlated with manner and 135 other fieldsHigh correlation
covid_secondary is highly correlated with manner and 134 other fieldsHigh correlation
hypoxic-ischemic_primary is highly correlated with manner and 136 other fieldsHigh correlation
hypoxia_primary is highly correlated with manner and 136 other fieldsHigh correlation
op-name_primary is highly correlated with manner and 133 other fieldsHigh correlation
drug_primary is highly correlated with manner and 136 other fieldsHigh correlation
nonfentanyl_opioid_primary is highly correlated with manner and 136 other fieldsHigh correlation
opiate_primary is highly correlated with manner and 136 other fieldsHigh correlation
alcohol_primary is highly correlated with manner and 135 other fieldsHigh correlation
eth_alc_primary is highly correlated with manner and 135 other fieldsHigh correlation
hypoxic-ischemic_secondary is highly correlated with manner and 135 other fieldsHigh correlation
hypoxia_secondary is highly correlated with manner and 135 other fieldsHigh correlation
amphetamine_primary is highly correlated with manner and 133 other fieldsHigh correlation
stimulant_primary is highly correlated with manner and 134 other fieldsHigh correlation
methamphetamine_primary is highly correlated with manner and 137 other fieldsHigh correlation
amphetamine_based_primary is highly correlated with manner and 133 other fieldsHigh correlation
hallucinogen_primary is highly correlated with manner and 133 other fieldsHigh correlation
benzodiazepine_primary is highly correlated with manner and 135 other fieldsHigh correlation
benzo_primary is highly correlated with manner and 135 other fieldsHigh correlation
sedative_primary is highly correlated with manner and 134 other fieldsHigh correlation
cocaine_primary is highly correlated with manner and 134 other fieldsHigh correlation
alcohol_secondary is highly correlated with manner and 137 other fieldsHigh correlation
drug_secondary is highly correlated with manner and 137 other fieldsHigh correlation
eth_alc_secondary is highly correlated with manner and 137 other fieldsHigh correlation
fentanyl-name_primary is highly correlated with manner and 136 other fieldsHigh correlation
fentanyl_primary is highly correlated with manner and 136 other fieldsHigh correlation
lorazepam_primary is highly correlated with manner and 135 other fieldsHigh correlation
clonazepam_primary is highly correlated with manner and 134 other fieldsHigh correlation
methadone_primary is highly correlated with manner and 135 other fieldsHigh correlation
anpp_primary is highly correlated with manner and 135 other fieldsHigh correlation
fen_analog_primary is highly correlated with manner and 135 other fieldsHigh correlation
heroin_primary is highly correlated with manner and 135 other fieldsHigh correlation
xylazine_primary is highly correlated with manner and 133 other fieldsHigh correlation
morphine_primary is highly correlated with manner and 135 other fieldsHigh correlation
alprazolam_primary is highly correlated with manner and 133 other fieldsHigh correlation
tramadol_primary is highly correlated with manner and 133 other fieldsHigh correlation
pcp_primary is highly correlated with manner and 135 other fieldsHigh correlation
buprenorphine_primary is highly correlated with manner and 136 other fieldsHigh correlation
hydromorphone_primary is highly correlated with manner and 135 other fieldsHigh correlation
hydrocodone_primary is highly correlated with manner and 135 other fieldsHigh correlation
inhalant_primary is highly correlated with manner and 135 other fieldsHigh correlation
acetylfentanyl_primary is highly correlated with manner and 135 other fieldsHigh correlation
mitragynine_primary is highly correlated with manner and 135 other fieldsHigh correlation
oxycodone_primary is highly correlated with manner and 135 other fieldsHigh correlation
cocaine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
stimulant_secondary is highly correlated with manner and 133 other fieldsHigh correlation
dihydrocodeine_primary is highly correlated with manner and 135 other fieldsHigh correlation
diazepam_primary is highly correlated with manner and 135 other fieldsHigh correlation
topiramate_primary is highly correlated with manner and 135 other fieldsHigh correlation
cyclobenzaprine_primary is highly correlated with manner and 135 other fieldsHigh correlation
toxic_primary is highly correlated with manner and 136 other fieldsHigh correlation
oxazepam_primary is highly correlated with manner and 135 other fieldsHigh correlation
temazepam_primary is highly correlated with manner and 135 other fieldsHigh correlation
carfentanyl_primary is highly correlated with manner and 135 other fieldsHigh correlation
ketamine_primary is highly correlated with manner and 135 other fieldsHigh correlation
oxymorphone_primary is highly correlated with manner and 135 other fieldsHigh correlation
codeine_primary is highly correlated with manner and 136 other fieldsHigh correlation
methocarbamol_primary is highly correlated with manner and 135 other fieldsHigh correlation
chlordiazepoxide_primary is highly correlated with manner and 135 other fieldsHigh correlation
acetylsalicylic acid_secondary is highly correlated with manner and 135 other fieldsHigh correlation
acetylsalicylic acid_primary is highly correlated with manner and 135 other fieldsHigh correlation
polysubstance_primary is highly correlated with manner and 135 other fieldsHigh correlation
levomethorphan_primary is highly correlated with manner and 135 other fieldsHigh correlation
levorphanol_primary is highly correlated with manner and 135 other fieldsHigh correlation
butyryl_fentanyl_primary is highly correlated with manner and 135 other fieldsHigh correlation
heroin_secondary is highly correlated with manner and 135 other fieldsHigh correlation
nonfentanyl_opioid_secondary is highly correlated with manner and 135 other fieldsHigh correlation
opiate_secondary is highly correlated with manner and 135 other fieldsHigh correlation
fentanyl-name_secondary is highly correlated with manner and 135 other fieldsHigh correlation
fentanyl_secondary is highly correlated with manner and 135 other fieldsHigh correlation
anpp_secondary is highly correlated with manner and 135 other fieldsHigh correlation
fen_analog_secondary is highly correlated with manner and 135 other fieldsHigh correlation
valerylfentanyl_primary is highly correlated with manner and 135 other fieldsHigh correlation
estazolam_primary is highly correlated with manner and 135 other fieldsHigh correlation
carisoprodol_primary is highly correlated with manner and 136 other fieldsHigh correlation
inhalant_secondary is highly correlated with manner and 135 other fieldsHigh correlation
lsd_primary is highly correlated with manner and 135 other fieldsHigh correlation
propoxyphene_primary is highly correlated with manner and 135 other fieldsHigh correlation
fbf_primary is highly correlated with manner and 135 other fieldsHigh correlation
barbiturates_primary is highly correlated with manner and 135 other fieldsHigh correlation
amphetamine_based_secondary is highly correlated with manner and 135 other fieldsHigh correlation
hallucinogen_secondary is highly correlated with manner and 135 other fieldsHigh correlation
marijuana_primary is highly correlated with manner and 135 other fieldsHigh correlation
cannabis_primary is highly correlated with manner and 135 other fieldsHigh correlation
op-name_secondary is highly correlated with manner and 135 other fieldsHigh correlation
benzodiazepine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
benzo_secondary is highly correlated with manner and 135 other fieldsHigh correlation
sedative_secondary is highly correlated with manner and 135 other fieldsHigh correlation
methylphenidate_primary is highly correlated with manner and 135 other fieldsHigh correlation
u-47700_primary is highly correlated with manner and 135 other fieldsHigh correlation
norfentanyl_primary is highly correlated with manner and 135 other fieldsHigh correlation
pcp_secondary is highly correlated with manner and 135 other fieldsHigh correlation
polysubstance_secondary is highly correlated with manner and 135 other fieldsHigh correlation
metaxalone_primary is highly correlated with manner and 135 other fieldsHigh correlation
tarpentadol_primary is highly correlated with manner and 135 other fieldsHigh correlation
methadone_secondary is highly correlated with manner and 135 other fieldsHigh correlation
cyclopropyl_fentanyl_primary is highly correlated with manner and 133 other fieldsHigh correlation
phentermine_primary is highly correlated with manner and 135 other fieldsHigh correlation
methoxyacetyl_fentanyl_primary is highly correlated with manner and 135 other fieldsHigh correlation
furanyl_fentanyl_primary is highly correlated with manner and 135 other fieldsHigh correlation
fbf_secondary is highly correlated with manner and 135 other fieldsHigh correlation
fibf_primary is highly correlated with manner and 135 other fieldsHigh correlation
flurazepam_primary is highly correlated with manner and 135 other fieldsHigh correlation
acetylfentanyl_secondary is highly correlated with manner and 135 other fieldsHigh correlation
u-49900_primary is highly correlated with manner and 135 other fieldsHigh correlation
tizanidine_primary is highly correlated with manner and 135 other fieldsHigh correlation
furanyl_fentanyl_secondary is highly correlated with manner and 135 other fieldsHigh correlation
lsd_secondary is highly correlated with manner and 135 other fieldsHigh correlation
methamphetamine_secondary is highly correlated with manner and 133 other fieldsHigh correlation
hydromorphone_secondary is highly correlated with manner and 135 other fieldsHigh correlation
hydrocodone_secondary is highly correlated with manner and 135 other fieldsHigh correlation
dihydrocodeine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
amphetamine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
meperidine_primary is highly correlated with manner and 135 other fieldsHigh correlation
clonazepam_secondary is highly correlated with manner and 135 other fieldsHigh correlation
alprazolam_secondary is highly correlated with manner and 135 other fieldsHigh correlation
xylazine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
mitragynine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
codeine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
ketamine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
diazepam_secondary is highly correlated with manner and 135 other fieldsHigh correlation
oxycodone_secondary is highly correlated with manner and 135 other fieldsHigh correlation
levomethorphan_secondary is highly correlated with manner and 135 other fieldsHigh correlation
levorphanol_secondary is highly correlated with manner and 135 other fieldsHigh correlation
lorazepam_secondary is highly correlated with manner and 135 other fieldsHigh correlation
marijuana_secondary is highly correlated with manner and 135 other fieldsHigh correlation
cannabis_secondary is highly correlated with manner and 135 other fieldsHigh correlation
oxymorphone_secondary is highly correlated with manner and 135 other fieldsHigh correlation
tramadol_secondary is highly correlated with manner and 135 other fieldsHigh correlation
cyclobenzaprine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
toxic_secondary is highly correlated with manner and 135 other fieldsHigh correlation
topiramate_secondary is highly correlated with manner and 135 other fieldsHigh correlation
morphine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
buprenorphine_secondary is highly correlated with manner and 135 other fieldsHigh correlation
incident_date_x has 1431 (2.4%) missing values Missing
commissioner_district has 7584 (12.5%) missing values Missing
residence_city has 1730 (2.9%) missing values Missing
residence_zip has 1636 (2.7%) missing values Missing
primarycause has 1045 (1.7%) missing values Missing
gunrelated has 1432 (2.4%) missing values Missing
opioids has 1432 (2.4%) missing values Missing
secondarycause has 36843 (60.9%) missing values Missing
primarycause_linea has 41285 (68.2%) missing values Missing
primarycause_lineb has 58090 (96.0%) missing values Missing
primarycause_linec has 60468 (99.9%) missing values Missing
geocoded_score has 54025 (89.2%) missing values Missing
geocoded_address has 53642 (88.6%) missing values Missing
full_address has 53642 (88.6%) missing values Missing
final_latitude has 1063 (1.8%) missing values Missing
final_longitude has 1063 (1.8%) missing values Missing
FIRST_COUNTY has 1551 (2.6%) missing values Missing
LANDUSE has 1551 (2.6%) missing values Missing
LANDUSE2 has 17174 (28.4%) missing values Missing
OS_MGMT has 59313 (98.0%) missing values Missing
FAC_NAME has 60349 (99.7%) missing values Missing
PLATTED has 58171 (96.1%) missing values Missing
MODIFIER has 58163 (96.1%) missing values Missing
STATEFP has 1341 (2.2%) missing values Missing
COUNTYFP has 1341 (2.2%) missing values Missing
TRACTCE has 1341 (2.2%) missing values Missing
GEOID has 1341 (2.2%) missing values Missing
NAME has 1341 (2.2%) missing values Missing
NAMELSAD has 1341 (2.2%) missing values Missing
MTFCC has 1341 (2.2%) missing values Missing
FUNCSTAT has 1341 (2.2%) missing values Missing
ALAND has 1341 (2.2%) missing values Missing
AWATER has 1341 (2.2%) missing values Missing
INTPTLAT has 1341 (2.2%) missing values Missing
INTPTLON has 1341 (2.2%) missing values Missing
OBJECTID_12 has 60236 (99.5%) missing values Missing
CFNAME has 60236 (99.5%) missing values Missing
CFTYPE has 60236 (99.5%) missing values Missing
CFSUBTYPE has 60236 (99.5%) missing values Missing
ADDRESS has 60236 (99.5%) missing values Missing
GNISCODE has 60236 (99.5%) missing values Missing
COMMENT has 60236 (99.5%) missing values Missing
SOURCE has 60236 (99.5%) missing values Missing
Jurisdicti has 60236 (99.5%) missing values Missing
Community has 60236 (99.5%) missing values Missing
landuse_name has 1551 (2.6%) missing values Missing
landuse_sub_name has 1551 (2.6%) missing values Missing
landuse_major_name has 1551 (2.6%) missing values Missing
motel has 53642 (88.6%) missing values Missing
primary_combined has 1045 (1.7%) missing values Missing
incident_date_y has 1490 (2.5%) missing values Missing
death_street has 1867 (3.1%) missing values Missing
death_city has 2173 (3.6%) missing values Missing
death_county has 27597 (45.6%) missing values Missing
death_state has 2362 (3.9%) missing values Missing
death_zip has 3285 (5.4%) missing values Missing
death_location has 24546 (40.5%) missing values Missing
death_location_1 has 6950 (11.5%) missing values Missing
record_id has 34187 (56.5%) missing values Missing
ALAND is highly skewed (γ1 = 26.68689517) Skewed
AWATER is highly skewed (γ1 = 38.48057344) Skewed
casenumber is uniformly distributed Uniform
incident_date_x is uniformly distributed Uniform
death_datetime is uniformly distributed Uniform
incident_date_y is uniformly distributed Uniform
death_date_y is uniformly distributed Uniform
record_id is uniformly distributed Uniform
casenumber has unique values Unique
age has 633 (1.0%) zeros Zeros
distance_between_points has 765 (1.3%) zeros Zeros
AWATER has 46101 (76.2%) zeros Zeros

Reproduction

Analysis started2022-01-11 15:43:18.953307
Analysis finished2022-01-11 15:46:42.697812
Duration3 minutes and 23.74 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

casenumber
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct60537
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
ME2021-11723
 
1
ME2017-06013
 
1
ME2017-06025
 
1
ME2017-06024
 
1
ME2017-06023
 
1
Other values (60532)
60532 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60537 ?
Unique (%)100.0%

Sample

1st rowME2021-11723
2nd rowME2021-11722
3rd rowME2021-11721
4th rowME2021-11720
5th rowME2021-11719

Common Values

ValueCountFrequency (%)
ME2021-117231
 
< 0.1%
ME2017-060131
 
< 0.1%
ME2017-060251
 
< 0.1%
ME2017-060241
 
< 0.1%
ME2017-060231
 
< 0.1%
ME2017-060221
 
< 0.1%
ME2017-060211
 
< 0.1%
ME2017-060201
 
< 0.1%
ME2017-060191
 
< 0.1%
ME2017-060181
 
< 0.1%
Other values (60527)60527
> 99.9%

Length

2022-01-11T10:46:42.723531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
me2021-117231
 
< 0.1%
me2021-117111
 
< 0.1%
me2021-116951
 
< 0.1%
me2021-116961
 
< 0.1%
me2021-117211
 
< 0.1%
me2021-117201
 
< 0.1%
me2021-117191
 
< 0.1%
me2021-117181
 
< 0.1%
me2021-117171
 
< 0.1%
me2021-117161
 
< 0.1%
Other values (60527)60527
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

incident_date_x
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct52231
Distinct (%)88.4%
Missing1431
Missing (%)2.4%
Memory size4.6 MiB
2020-04-16T00:00:00.000
 
34
2020-05-04T00:00:00.000
 
34
2020-04-29T00:00:00.000
 
33
2020-04-20T00:00:00.000
 
32
2020-04-24T00:00:00.000
 
32
Other values (52226)
58941 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49331 ?
Unique (%)83.5%

Sample

1st row2021-12-13T16:00:00.000
2nd row2021-12-15T02:55:00.000
3rd row2021-12-07T09:38:00.000
4th row2021-12-14T20:00:00.000
5th row2021-12-15T00:01:00.000

Common Values

ValueCountFrequency (%)
2020-04-16T00:00:00.00034
 
0.1%
2020-05-04T00:00:00.00034
 
0.1%
2020-04-29T00:00:00.00033
 
0.1%
2020-04-20T00:00:00.00032
 
0.1%
2020-04-24T00:00:00.00032
 
0.1%
2020-05-05T00:00:00.00032
 
0.1%
2020-04-19T00:00:00.00032
 
0.1%
2020-04-23T00:00:00.00030
 
< 0.1%
2020-12-14T00:00:00.00029
 
< 0.1%
2020-04-28T00:00:00.00028
 
< 0.1%
Other values (52221)58790
97.1%
(Missing)1431
 
2.4%

Length

2022-01-11T10:46:42.762215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-04-16t00:00:00.00034
 
0.1%
2020-05-04t00:00:00.00034
 
0.1%
2020-04-29t00:00:00.00033
 
0.1%
2020-04-20t00:00:00.00032
 
0.1%
2020-04-24t00:00:00.00032
 
0.1%
2020-05-05t00:00:00.00032
 
0.1%
2020-04-19t00:00:00.00032
 
0.1%
2020-04-23t00:00:00.00030
 
0.1%
2020-12-14t00:00:00.00029
 
< 0.1%
2020-04-28t00:00:00.00028
 
< 0.1%
Other values (52221)58790
99.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_date_x
Categorical

HIGH CARDINALITY

Distinct2699
Distinct (%)4.5%
Missing59
Missing (%)0.1%
Memory size3.9 MiB
2020-05-07
 
111
2020-05-03
 
109
2020-05-04
 
108
2020-05-09
 
108
2020-05-02
 
106
Other values (2694)
59936 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st row2021-12-14
2nd row2021-12-15
3rd row2021-12-15
4th row2021-12-15
5th row2021-12-15

Common Values

ValueCountFrequency (%)
2020-05-07111
 
0.2%
2020-05-03109
 
0.2%
2020-05-04108
 
0.2%
2020-05-09108
 
0.2%
2020-05-02106
 
0.2%
2020-05-14105
 
0.2%
2020-05-05103
 
0.2%
2020-05-15102
 
0.2%
2020-04-28101
 
0.2%
2020-04-2499
 
0.2%
Other values (2689)59426
98.2%

Length

2022-01-11T10:46:42.799605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-05-07111
 
0.2%
2020-05-03109
 
0.2%
2020-05-04108
 
0.2%
2020-05-09108
 
0.2%
2020-05-02106
 
0.2%
2020-05-14105
 
0.2%
2020-05-05103
 
0.2%
2020-05-15102
 
0.2%
2020-04-28101
 
0.2%
2020-04-2499
 
0.2%
Other values (2689)59426
98.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct110
Distinct (%)0.2%
Missing346
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean55.80367497
Minimum0
Maximum109
Zeros633
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:42.843845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q140
median58
Q371
95-th percentile90
Maximum109
Range109
Interquartile range (IQR)31

Descriptive statistics

Standard deviation21.4586312
Coefficient of variation (CV)0.3845379577
Kurtosis-0.5373261154
Mean55.80367497
Median Absolute Deviation (MAD)15
Skewness-0.2299097828
Sum3358879
Variance460.4728531
MonotonicityNot monotonic
2022-01-11T10:46:42.894946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
581290
 
2.1%
591255
 
2.1%
621208
 
2.0%
601207
 
2.0%
611197
 
2.0%
571182
 
2.0%
641181
 
2.0%
561166
 
1.9%
631157
 
1.9%
541103
 
1.8%
Other values (100)48245
79.7%
ValueCountFrequency (%)
0633
1.0%
171
 
0.1%
262
 
0.1%
338
 
0.1%
427
 
< 0.1%
534
 
0.1%
618
 
< 0.1%
717
 
< 0.1%
820
 
< 0.1%
922
 
< 0.1%
ValueCountFrequency (%)
1091
 
< 0.1%
1081
 
< 0.1%
1071
 
< 0.1%
1064
 
< 0.1%
1055
 
< 0.1%
1043
 
< 0.1%
10326
< 0.1%
10215
 
< 0.1%
10143
0.1%
10063
0.1%

gender
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing237
Missing (%)0.4%
Memory size3.6 MiB
Male
41830 
Female
18444 
Unknown
 
26

Length

Max length7
Median length4
Mean length4.613034826
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male41830
69.1%
Female18444
30.5%
Unknown26
 
< 0.1%
(Missing)237
 
0.4%

Length

2022-01-11T10:46:42.943291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:42.966221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
male41830
69.4%
female18444
30.6%
unknown26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

race
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing359
Missing (%)0.6%
Memory size3.6 MiB
White
33131 
Black
24605 
Asian
 
1363
Other
 
825
Unknown
 
209

Length

Max length10
Median length5
Mean length5.010684968
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWhite
2nd rowBlack
3rd rowWhite
4th rowWhite
5th rowBlack

Common Values

ValueCountFrequency (%)
White33131
54.7%
Black24605
40.6%
Asian1363
 
2.3%
Other825
 
1.4%
Unknown209
 
0.3%
Am. Indian45
 
0.1%
(Missing)359
 
0.6%

Length

2022-01-11T10:46:43.337174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:43.366098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
white33131
55.0%
black24605
40.9%
asian1363
 
2.3%
other825
 
1.4%
unknown209
 
0.3%
am45
 
0.1%
indian45
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

latino
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
0
52832 
1
7705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
052832
87.3%
17705
 
12.7%

Length

2022-01-11T10:46:43.404678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:43.430924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
052832
87.3%
17705
 
12.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cold_related
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
0
60164 
1
 
373

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
060164
99.4%
1373
 
0.6%

Length

2022-01-11T10:46:43.459672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:43.486733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
060164
99.4%
1373
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

heat_related
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
0
60510 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
060510
> 99.9%
127
 
< 0.1%

Length

2022-01-11T10:46:43.515890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:43.542853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
060510
> 99.9%
127
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

commissioner_district
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)< 0.1%
Missing7584
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean7.485864824
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:43.568870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q311
95-th percentile16
Maximum17
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.965155953
Coefficient of variation (CV)0.6632708539
Kurtosis-1.095970709
Mean7.485864824
Median Absolute Deviation (MAD)4
Skewness0.3894255968
Sum396399
Variance24.65277364
MonotonicityNot monotonic
2022-01-11T10:46:43.608592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
25872
 
9.7%
15156
 
8.5%
34372
 
7.2%
54039
 
6.7%
44005
 
6.6%
112925
 
4.8%
102903
 
4.8%
72736
 
4.5%
82705
 
4.5%
62626
 
4.3%
Other values (7)15614
25.8%
(Missing)7584
12.5%
ValueCountFrequency (%)
15156
8.5%
25872
9.7%
34372
7.2%
44005
6.6%
54039
6.7%
62626
4.3%
72736
4.5%
82705
4.5%
92491
4.1%
102903
4.8%
ValueCountFrequency (%)
172332
3.9%
162390
3.9%
151720
2.8%
141844
3.0%
132452
4.1%
122385
3.9%
112925
4.8%
102903
4.8%
92491
4.1%
82705
4.5%

residence_city
Categorical

HIGH CARDINALITY
MISSING

Distinct1124
Distinct (%)1.9%
Missing1730
Missing (%)2.9%
Memory size3.7 MiB
Chicago
32884 
Des Plaines
 
805
Oak Lawn
 
681
Cicero
 
675
Arlington Heights
 
543
Other values (1119)
23219 

Length

Max length22
Median length7
Mean length8.174418011
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique591 ?
Unique (%)1.0%

Sample

1st rowChicago
2nd rowChicago
3rd rowChicago
4th rowChicago Ridge
5th rowChicago

Common Values

ValueCountFrequency (%)
Chicago32884
54.3%
Des Plaines805
 
1.3%
Oak Lawn681
 
1.1%
Cicero675
 
1.1%
Arlington Heights543
 
0.9%
Berwyn529
 
0.9%
Evanston506
 
0.8%
Skokie459
 
0.8%
Niles455
 
0.8%
Orland Park453
 
0.7%
Other values (1114)20817
34.4%
(Missing)1730
 
2.9%

Length

2022-01-11T10:46:43.662060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago33701
47.4%
park3669
 
5.2%
heights1413
 
2.0%
oak1278
 
1.8%
des810
 
1.1%
plaines805
 
1.1%
grove764
 
1.1%
forest712
 
1.0%
lawn682
 
1.0%
cicero676
 
1.0%
Other values (1064)26616
37.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

residence_zip
Real number (ℝ≥0)

MISSING

Distinct1550
Distinct (%)2.6%
Missing1636
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean60163.08489
Minimum0
Maximum99999
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:43.716337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60016
Q160425
median60618
Q360637
95-th percentile60707
Maximum99999
Range99999
Interquartile range (IQR)212

Descriptive statistics

Standard deviation3821.30714
Coefficient of variation (CV)0.06351581119
Kurtosis96.64912257
Mean60163.08489
Median Absolute Deviation (MAD)33
Skewness-6.006921363
Sum3543665863
Variance14602388.26
MonotonicityNot monotonic
2022-01-11T10:46:43.921144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
606281520
 
2.5%
606231390
 
2.3%
606201341
 
2.2%
606191286
 
2.1%
606291258
 
2.1%
606441226
 
2.0%
606171212
 
2.0%
606241070
 
1.8%
606511065
 
1.8%
606491023
 
1.7%
Other values (1540)46510
76.8%
(Missing)1636
 
2.7%
ValueCountFrequency (%)
06
< 0.1%
6271
 
< 0.1%
7391
 
< 0.1%
9011
 
< 0.1%
9761
 
< 0.1%
9791
 
< 0.1%
11091
 
< 0.1%
13311
 
< 0.1%
16021
 
< 0.1%
16101
 
< 0.1%
ValueCountFrequency (%)
999992
< 0.1%
995151
< 0.1%
992401
< 0.1%
986621
< 0.1%
983121
< 0.1%
981221
< 0.1%
981181
< 0.1%
981121
< 0.1%
980771
< 0.1%
980361
< 0.1%

manner
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing510
Missing (%)0.8%
Memory size3.7 MiB
NATURAL
30392 
ACCIDENT
18165 
HOMICIDE
6067 
SUICIDE
3446 
UNDETERMINED
 
1035

Length

Max length12
Median length7
Mean length7.489896213
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNATURAL
2nd rowPENDING
3rd rowNATURAL
4th rowNATURAL
5th rowACCIDENT

Common Values

ValueCountFrequency (%)
NATURAL30392
50.2%
ACCIDENT18165
30.0%
HOMICIDE6067
 
10.0%
SUICIDE3446
 
5.7%
UNDETERMINED1035
 
1.7%
PENDING922
 
1.5%
(Missing)510
 
0.8%

Length

2022-01-11T10:46:43.967410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:43.995097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
natural30392
50.6%
accident18165
30.3%
homicide6067
 
10.1%
suicide3446
 
5.7%
undetermined1035
 
1.7%
pending922
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

primarycause
Categorical

HIGH CARDINALITY
MISSING

Distinct8798
Distinct (%)14.8%
Missing1045
Missing (%)1.7%
Memory size5.1 MiB
ORGANIC CARDIOVASCULAR DISEASE
5469 
PNEUMONIA
 
4114
MULTIPLE GUNSHOT WOUNDS
 
3059
HYPERTENSIVE CARDIOVASCULAR DISEASE
 
2535
MULTIPLE INJURIES
 
1991
Other values (8793)
42324 

Length

Max length191
Median length30
Mean length32.62284005
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6908 ?
Unique (%)11.6%

Sample

1st rowATHEROSCLEROTIC CARDIOVASCULAR DISEASE
2nd rowORGANIC CARDIOVASCULAR DISEASE
3rd rowORGANIC CARDIOVASCULAR DISEASE
4th rowMULTIPLE INJURIES
5th rowMULTIPLE BLUNT FORCE INJURIES

Common Values

ValueCountFrequency (%)
ORGANIC CARDIOVASCULAR DISEASE5469
 
9.0%
PNEUMONIA4114
 
6.8%
MULTIPLE GUNSHOT WOUNDS3059
 
5.1%
HYPERTENSIVE CARDIOVASCULAR DISEASE2535
 
4.2%
MULTIPLE INJURIES1991
 
3.3%
NOVEL CORONA (COVID-19) VIRAL INFECTION1905
 
3.1%
ACUTE HYPOXIC RESPIRATORY FAILURE1447
 
2.4%
HYPERTENSIVE ARTERIOSCLEROTIC CARDIOVASCULAR DISEASE1393
 
2.3%
ARTERIOSCLEROTIC CARDIOVASCULAR DISEASE1183
 
2.0%
GUNSHOT WOUND OF HEAD879
 
1.5%
Other values (8788)35517
58.7%
(Missing)1045
 
1.7%

Length

2022-01-11T10:46:44.047303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
disease13935
 
6.3%
cardiovascular13329
 
6.0%
fentanyl9303
 
4.2%
and9050
 
4.1%
toxicity8938
 
4.0%
of8582
 
3.9%
multiple6860
 
3.1%
gunshot6353
 
2.9%
organic6103
 
2.8%
combined5827
 
2.6%
Other values (2032)133347
60.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

gunrelated
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1432
Missing (%)2.4%
Memory size3.4 MiB
0.0
52635 
1.0
6470 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.052635
86.9%
1.06470
 
10.7%
(Missing)1432
 
2.4%

Length

2022-01-11T10:46:44.097299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:44.122706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.052635
89.1%
1.06470
 
10.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

opioids
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1432
Missing (%)2.4%
Memory size3.4 MiB
0.0
50172 
1.0
8933 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.050172
82.9%
1.08933
 
14.8%
(Missing)1432
 
2.4%

Length

2022-01-11T10:46:44.150680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:44.176251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.050172
84.9%
1.08933
 
15.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

secondarycause
Categorical

HIGH CARDINALITY
MISSING

Distinct9266
Distinct (%)39.1%
Missing36843
Missing (%)60.9%
Memory size3.5 MiB
DIABETES MELLITUS
 
1522
OBESITY
 
1296
HYPERTENSION
 
898
CHRONIC ETHANOLISM
 
640
HYPERTENSIVE CARDIOVASCULAR DISEASE
 
626
Other values (9261)
18712 

Length

Max length247
Median length37
Mean length45.92909597
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8025 ?
Unique (%)33.9%

Sample

1st rowOBESITY
2nd rowHYPERTENSION, DIABETES MELLITUS
3rd rowHYPERTENSION
4th rowHYPERTENSION, CORONARY ARTERY DISEASE, DIABETES MELLITUS, CHRONIC KIDNEY DISEASE, ASTHMA, ATRIAL FIBRILLATION
5th rowCORONARY ARTERY DISEASE, HYPERTENSION, HEPATIC STEATOSIS

Common Values

ValueCountFrequency (%)
DIABETES MELLITUS1522
 
2.5%
OBESITY1296
 
2.1%
HYPERTENSION898
 
1.5%
CHRONIC ETHANOLISM640
 
1.1%
HYPERTENSIVE CARDIOVASCULAR DISEASE626
 
1.0%
HYPERTENSION, DIABETES MELLITUS416
 
0.7%
CHRONIC OBSTRUCTIVE PULMONARY DISEASE360
 
0.6%
HYPERTENSIVE ARTERIOSCLEROTIC CARDIOVASCULAR DISEASE322
 
0.5%
DIABETES MELLITUS, HYPERTENSION221
 
0.4%
MORBID OBESITY200
 
0.3%
Other values (9256)17193
28.4%
(Missing)36843
60.9%

Length

2022-01-11T10:46:44.216023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
disease12342
 
10.7%
diabetes8148
 
7.0%
mellitus7771
 
6.7%
hypertension7659
 
6.6%
chronic6766
 
5.8%
cardiovascular4571
 
3.9%
hypertensive3887
 
3.4%
obesity3690
 
3.2%
pulmonary2918
 
2.5%
obstructive2844
 
2.5%
Other values (2130)55141
47.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

primarycause_linea
Categorical

HIGH CARDINALITY
MISSING

Distinct2526
Distinct (%)13.1%
Missing41285
Missing (%)68.2%
Memory size2.8 MiB
NOVEL CORONA (COVID-19) VIRAL INFECTION
4772 
NOVEL CORONA (COVID-19) VIRUS INFECTION
1805 
FALL
1667 
PNEUMONIA
1647 
MOTOR VEHICLE COLLISION
 
639
Other values (2521)
8722 

Length

Max length174
Median length32
Mean length27.03724288
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1975 ?
Unique (%)10.3%

Sample

1st rowSPORTS UTILITY VEHICLE STRIKING PEDESTRIAN
2nd rowMOTOR VEHICLE COLLISION
3rd rowFALL
4th rowNOVEL CORONA (COVID-19) VIRAL INFECTION
5th rowNOVEL CORONA (COVID-19) VIRAL INFECTION

Common Values

ValueCountFrequency (%)
NOVEL CORONA (COVID-19) VIRAL INFECTION4772
 
7.9%
NOVEL CORONA (COVID-19) VIRUS INFECTION1805
 
3.0%
FALL1667
 
2.8%
PNEUMONIA1647
 
2.7%
MOTOR VEHICLE COLLISION639
 
1.1%
HANGING397
 
0.7%
MOTOR VEHICLE STRIKING PEDESTRIAN362
 
0.6%
FALL FROM HEIGHT233
 
0.4%
FALL DOWN STAIRS205
 
0.3%
MOTOR VEHICLE STRIKING FIXED OBJECT191
 
0.3%
Other values (2516)7334
 
12.1%
(Missing)41285
68.2%

Length

2022-01-11T10:46:44.276875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
covid-196825
 
10.0%
novel6824
 
10.0%
corona6813
 
10.0%
infection6698
 
9.8%
viral4978
 
7.3%
fall2926
 
4.3%
vehicle2165
 
3.2%
motor2024
 
3.0%
pneumonia1926
 
2.8%
virus1870
 
2.7%
Other values (1519)25218
36.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

primarycause_lineb
Categorical

HIGH CARDINALITY
MISSING

Distinct377
Distinct (%)15.4%
Missing58090
Missing (%)96.0%
Memory size2.0 MiB
NOVEL CORONA (COVID-19) VIRAL INFECTION
1356 
NOVEL CORONA (COVID-19) VIRUS INFECTION
343 
HYPERTENSIVE CARDIOVASCULAR DISEASE
 
70
HOUSE FIRE
 
46
.
 
36
Other values (372)
596 

Length

Max length82
Median length39
Mean length36.00040866
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique305 ?
Unique (%)12.5%

Sample

1st rowNOVEL CORONA (COVID-19) VIRAL INFECTION
2nd rowNOVEL CORONA (COVID-19) VIRAL INFECTION
3rd rowNOVEL CORONA (COVID-19) VIRAL INFECTION
4th rowNOVEL CORONA (COVID-19) VIRAL INFECTION
5th rowNOVEL CORONA (COVID-19) VIRAL INFECTION

Common Values

ValueCountFrequency (%)
NOVEL CORONA (COVID-19) VIRAL INFECTION1356
 
2.2%
NOVEL CORONA (COVID-19) VIRUS INFECTION343
 
0.6%
HYPERTENSIVE CARDIOVASCULAR DISEASE70
 
0.1%
HOUSE FIRE46
 
0.1%
.36
 
0.1%
HYPERTENSIVE AND ATHEROSCLEROTIC CARDIOVASCULAR DISEASE31
 
0.1%
FALL23
 
< 0.1%
CARELESS USE OF SMOKING MATERIALS16
 
< 0.1%
CHRONIC ETHANOLISM16
 
< 0.1%
APARTMENT FIRE13
 
< 0.1%
Other values (367)497
 
0.8%
(Missing)58090
96.0%

Length

2022-01-11T10:46:44.339898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
novel1725
15.2%
covid-191724
15.2%
infection1723
15.2%
corona1718
15.2%
viral1365
12.0%
virus357
 
3.2%
disease159
 
1.4%
cardiovascular132
 
1.2%
hypertensive123
 
1.1%
to84
 
0.7%
Other values (496)2221
19.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

primarycause_linec
Categorical

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)10.1%
Missing60468
Missing (%)99.9%
Memory size1.8 MiB
.
62 
END STAGE RENAL DISEASE
 
2
HYPERTENSION, DIABETES MELLITUS, AND CORONARY ARTERY DISEASE
 
1
HEPATITIS C
 
1
HYPERTENSIVE CARDIOVASCULAR DISEASE
 
1
Other values (2)
 
2

Length

Max length60
Median length1
Mean length3.405797101
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)7.2%

Sample

1st row.
2nd rowHYPERTENSION, DIABETES MELLITUS, AND CORONARY ARTERY DISEASE
3rd row.
4th row.
5th row.

Common Values

ValueCountFrequency (%)
.62
 
0.1%
END STAGE RENAL DISEASE2
 
< 0.1%
HYPERTENSION, DIABETES MELLITUS, AND CORONARY ARTERY DISEASE1
 
< 0.1%
HEPATITIS C1
 
< 0.1%
HYPERTENSIVE CARDIOVASCULAR DISEASE1
 
< 0.1%
..1
 
< 0.1%
HIGH BLOOD PRESSURE1
 
< 0.1%
(Missing)60468
99.9%

Length

2022-01-11T10:46:44.391702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:44.420863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
63
73.3%
disease4
 
4.7%
stage2
 
2.3%
renal2
 
2.3%
end2
 
2.3%
hepatitis1
 
1.2%
blood1
 
1.2%
high1
 
1.2%
cardiovascular1
 
1.2%
hypertensive1
 
1.2%
Other values (8)8
 
9.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

geocoded_score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1387
Distinct (%)21.3%
Missing54025
Missing (%)89.2%
Infinite0
Infinite (%)0.0%
Mean93.86565264
Minimum70
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:44.469133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile77
Q190
median97.3
Q3100
95-th percentile100
Maximum100
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.621782614
Coefficient of variation (CV)0.08119884536
Kurtosis0.2186480964
Mean93.86565264
Median Absolute Deviation (MAD)2.7
Skewness-1.194861257
Sum611253.13
Variance58.09157021
MonotonicityNot monotonic
2022-01-11T10:46:44.520487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1002098
 
3.5%
77261
 
0.4%
98.53114
 
0.2%
99.4375
 
0.1%
98.5162
 
0.1%
8047
 
0.1%
98.940
 
0.1%
99.538
 
0.1%
99.3931
 
0.1%
98.5729
 
< 0.1%
Other values (1377)3717
 
6.1%
(Missing)54025
89.2%
ValueCountFrequency (%)
706
< 0.1%
70.072
 
< 0.1%
70.181
 
< 0.1%
70.222
 
< 0.1%
70.361
 
< 0.1%
70.661
 
< 0.1%
70.671
 
< 0.1%
70.912
 
< 0.1%
71.052
 
< 0.1%
71.191
 
< 0.1%
ValueCountFrequency (%)
1002098
3.5%
99.9914
 
< 0.1%
99.951
 
< 0.1%
99.921
 
< 0.1%
99.911
 
< 0.1%
99.95
 
< 0.1%
99.864
 
< 0.1%
99.752
 
< 0.1%
99.721
 
< 0.1%
99.693
 
< 0.1%

geocoded_address
Categorical

HIGH CARDINALITY
MISSING

Distinct5226
Distinct (%)75.8%
Missing53642
Missing (%)88.6%
Memory size2.3 MiB
UNKNOWN
 
154
60666, Chicago, Illinois
 
46
10000 W Ohare Ave, Chicago, Illinois, 60666
 
36
Chicago, Illinois
 
28
60612, Chicago, Illinois
 
25
Other values (5221)
6606 

Length

Max length76
Median length42
Mean length38.58781726
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4661 ?
Unique (%)67.6%

Sample

1st rowTri State Toll Rd, Hinsdale, Illinois, 60521
2nd rowW Belle Plaine Ave & N Lawndale Ave, Chicago, Illinois, 60618
3rd rowS Anna Marie Dr, Mount Prospect, Illinois, 60056
4th rowPlymouth Place
5th row3156 N Olcott Ave, Elmwood Park, Illinois, 60707

Common Values

ValueCountFrequency (%)
UNKNOWN154
 
0.3%
60666, Chicago, Illinois46
 
0.1%
10000 W Ohare Ave, Chicago, Illinois, 6066636
 
0.1%
Chicago, Illinois28
 
< 0.1%
60612, Chicago, Illinois25
 
< 0.1%
Gary, Indiana23
 
< 0.1%
60617, Chicago, Illinois20
 
< 0.1%
1666 Checker Rd, Lake Zurich, Illinois, 6004717
 
< 0.1%
800 S River Rd, Des Plaines, Illinois, 6001616
 
< 0.1%
2308 Old Hicks Rd, Lake Zurich, Illinois, 6004716
 
< 0.1%
Other values (5216)6514
 
10.8%
(Missing)53642
88.6%

Length

2022-01-11T10:46:44.578558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
illinois5989
 
14.3%
chicago2317
 
5.5%
ave1733
 
4.1%
s1267
 
3.0%
st1141
 
2.7%
w1103
 
2.6%
rd904
 
2.2%
dr704
 
1.7%
n657
 
1.6%
park579
 
1.4%
Other values (5235)25588
60.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

full_address
Categorical

HIGH CARDINALITY
MISSING

Distinct6311
Distinct (%)91.5%
Missing53642
Missing (%)88.6%
Memory size2.2 MiB
UNKNOWN
 
154
CHICAGO
 
28
CHICAGO 60612
 
16
CHICAGO 60608
 
10
10000 w ohare ave CHICAGO 60666
 
9
Other values (6306)
6678 

Length

Max length73
Median length35
Mean length35.67585207
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6102 ?
Unique (%)88.5%

Sample

1st row5800 tri state toll rd 5801 HINSDALE 60521
2nd rowbelle plaine and lawndale avenues CHICAGO 60618
3rd row1528 s anna marie dr MOUNT PROSPECT 60056
4th rowplymouth place 315 n la grange rd LA GRANGE PARK 60526
5th row3156 n olcott ELMWOOD PARK 60707

Common Values

ValueCountFrequency (%)
UNKNOWN154
 
0.3%
CHICAGO28
 
< 0.1%
CHICAGO 6061216
 
< 0.1%
CHICAGO 6060810
 
< 0.1%
10000 w ohare ave CHICAGO 606669
 
< 0.1%
UNKNOWN 000009
 
< 0.1%
CHICAGO 606179
 
< 0.1%
CHICAGO 606237
 
< 0.1%
CHICAGO 606377
 
< 0.1%
HARVEY 604267
 
< 0.1%
Other values (6301)6639
 
11.0%
(Missing)53642
88.6%

Length

2022-01-11T10:46:44.640182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago2402
 
5.8%
s913
 
2.2%
avenue732
 
1.8%
w721
 
1.7%
street687
 
1.7%
park602
 
1.4%
ave596
 
1.4%
road551
 
1.3%
drive511
 
1.2%
south500
 
1.2%
Other values (7211)33341
80.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recovered
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
0
54025 
1
6512 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
054025
89.2%
16512
 
10.8%

Length

2022-01-11T10:46:44.688973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:44.714412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
054025
89.2%
16512
 
10.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

final_latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct47764
Distinct (%)80.3%
Missing1063
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean41.83959272
Minimum36.99654
Maximum42.56886002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:44.747001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum36.99654
5-th percentile41.57924343
Q141.74859839
median41.85946137
Q341.94565825
95-th percentile42.06800081
Maximum42.56886002
Range5.57232002
Interquartile range (IQR)0.1970598625

Descriptive statistics

Standard deviation0.1816325608
Coefficient of variation (CV)0.004341164647
Kurtosis121.4244961
Mean41.83959272
Median Absolute Deviation (MAD)0.101682715
Skewness-6.017611364
Sum2488367.937
Variance0.03299038712
MonotonicityNot monotonic
2022-01-11T10:46:44.798019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.9574516158
 
0.1%
41.7592185551
 
0.1%
41.997574746
 
0.1%
42.002093344
 
0.1%
42.0579143143
 
0.1%
41.8642343240
 
0.1%
41.9998142640
 
0.1%
41.676078739
 
0.1%
41.8809647538
 
0.1%
41.9738043837
 
0.1%
Other values (47754)59038
97.5%
(Missing)1063
 
1.8%
ValueCountFrequency (%)
36.996542
< 0.1%
37.110641
< 0.1%
37.114265051
< 0.1%
37.217471
< 0.1%
37.324731
< 0.1%
37.345131
< 0.1%
37.600570021
< 0.1%
37.639541
< 0.1%
37.65970651
< 0.1%
37.723571
< 0.1%
ValueCountFrequency (%)
42.568860021
< 0.1%
42.564720991
< 0.1%
42.554241181
< 0.1%
42.516872981
< 0.1%
42.512817981
< 0.1%
42.50060141
< 0.1%
42.489154281
< 0.1%
42.484977331
< 0.1%
42.478791
< 0.1%
42.477144061
< 0.1%

final_longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct47753
Distinct (%)80.3%
Missing1063
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean-87.73878973
Minimum-91.57094725
Maximum-87.38005298
Zeros0
Zeros (%)0.0%
Negative59474
Negative (%)98.2%
Memory size473.1 KiB
2022-01-11T10:46:44.852175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-91.57094725
5-th percentile-88.00814461
Q1-87.78990185
median-87.70966626
Q3-87.64976182
95-th percentile-87.57826764
Maximum-87.38005298
Range4.19089427
Interquartile range (IQR)0.140140035

Descriptive statistics

Standard deviation0.1593166648
Coefficient of variation (CV)-0.001815806501
Kurtosis96.98684359
Mean-87.73878973
Median Absolute Deviation (MAD)0.066535465
Skewness-6.164016171
Sum-5218176.78
Variance0.02538179968
MonotonicityNot monotonic
2022-01-11T10:46:44.984308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.8031321858
 
0.1%
-87.5597487551
 
0.1%
-87.887646446
 
0.1%
-87.7916879844
 
0.1%
-87.8391317242
 
0.1%
-88.0570884840
 
0.1%
-87.8257600740
 
0.1%
-87.8120915239
 
0.1%
-87.6682247838
 
0.1%
-87.7277821937
 
0.1%
Other values (47743)59039
97.5%
(Missing)1063
 
1.8%
ValueCountFrequency (%)
-91.570947251
 
< 0.1%
-91.538054
< 0.1%
-91.537591
 
< 0.1%
-91.256044911
 
< 0.1%
-91.120571
 
< 0.1%
-91.11688991
 
< 0.1%
-91.014712
< 0.1%
-90.870178221
 
< 0.1%
-90.756589931
 
< 0.1%
-90.721851
 
< 0.1%
ValueCountFrequency (%)
-87.380052981
 
< 0.1%
-87.380364953
< 0.1%
-87.380504971
 
< 0.1%
-87.38133491
 
< 0.1%
-87.381804921
 
< 0.1%
-87.38212491
 
< 0.1%
-87.382159981
 
< 0.1%
-87.382174032
< 0.1%
-87.382250581
 
< 0.1%
-87.382962441
 
< 0.1%

distance_between_points
Real number (ℝ)

ZEROS

Distinct23646
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4785.448391
Minimum-1
Maximum10145.3359
Zeros765
Zeros (%)1.3%
Negative30269
Negative (%)50.0%
Memory size473.1 KiB
2022-01-11T10:46:45.036384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q39817.621112
95-th percentile9833.256104
Maximum10145.3359
Range10146.3359
Interquartile range (IQR)9818.621112

Descriptive statistics

Standard deviation4909.09288
Coefficient of variation (CV)1.025837597
Kurtosis-1.997491174
Mean4785.448391
Median Absolute Deviation (MAD)0
Skewness0.05060819996
Sum289696689.3
Variance24099192.9
MonotonicityNot monotonic
2022-01-11T10:46:45.089179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-130269
50.0%
0765
 
1.3%
9825.43340255
 
0.1%
9804.69002643
 
0.1%
9824.61543338
 
0.1%
9828.69104838
 
0.1%
9846.53253836
 
0.1%
9820.73681936
 
0.1%
9819.25341935
 
0.1%
9822.69010434
 
0.1%
Other values (23636)29188
48.2%
ValueCountFrequency (%)
-130269
50.0%
0765
 
1.3%
9785.9582341
 
< 0.1%
9788.8462171
 
< 0.1%
9789.0039741
 
< 0.1%
9789.027261
 
< 0.1%
9789.1988091
 
< 0.1%
9789.4091186
 
< 0.1%
9789.4961181
 
< 0.1%
9789.5456931
 
< 0.1%
ValueCountFrequency (%)
10145.33591
< 0.1%
10138.691281
< 0.1%
10103.599171
< 0.1%
10067.215331
< 0.1%
10062.838571
< 0.1%
10054.656961
< 0.1%
10054.114121
< 0.1%
10053.024551
< 0.1%
10049.707541
< 0.1%
10044.07411
< 0.1%

closest_pharmacy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4943
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8747.852363
Minimum0
Maximum9801.384878
Zeros57
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:45.142511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.678895464
Q19801.384878
median9801.384878
Q39801.384878
95-th percentile9801.384878
Maximum9801.384878
Range9801.384878
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3034.553886
Coefficient of variation (CV)0.3468913009
Kurtosis4.417708146
Mean8747.852363
Median Absolute Deviation (MAD)0
Skewness-2.533263203
Sum529568738.5
Variance9208517.289
MonotonicityNot monotonic
2022-01-11T10:46:45.195397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9801.38487854025
89.2%
057
 
0.1%
1.44542022946
 
0.1%
2.43168519636
 
0.1%
0.07894932928
 
< 0.1%
0.29814432925
 
< 0.1%
10.3320090223
 
< 0.1%
0.67826027320
 
< 0.1%
0.46506136717
 
< 0.1%
1.05274424917
 
< 0.1%
Other values (4933)6243
 
10.3%
ValueCountFrequency (%)
057
0.1%
9.49307 × 10-57
 
< 0.1%
0.0057211033
 
< 0.1%
0.0092380821
 
< 0.1%
0.0097251891
 
< 0.1%
0.0155016391
 
< 0.1%
0.0195370581
 
< 0.1%
0.0197081321
 
< 0.1%
0.0198500595
 
< 0.1%
0.0205800541
 
< 0.1%
ValueCountFrequency (%)
9801.38487854025
89.2%
572.60149242
 
< 0.1%
519.93825381
 
< 0.1%
502.24642141
 
< 0.1%
501.62942021
 
< 0.1%
490.85166111
 
< 0.1%
478.28638371
 
< 0.1%
477.66835191
 
< 0.1%
469.16296881
 
< 0.1%
461.35478591
 
< 0.1%

FIRST_COUNTY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)< 0.1%
Missing1551
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean32.96917913
Minimum31
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:45.238253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile31
Q131
median31
Q331
95-th percentile31
Maximum197
Range166
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.5587956
Coefficient of variation (CV)0.4719194111
Kurtosis90.52878275
Mean32.96917913
Median Absolute Deviation (MAD)0
Skewness9.282568302
Sum1944720
Variance242.0761205
MonotonicityNot monotonic
2022-01-11T10:46:45.273786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3157458
94.9%
43508
 
0.8%
197433
 
0.7%
97367
 
0.6%
89150
 
0.2%
11151
 
0.1%
9319
 
< 0.1%
(Missing)1551
 
2.6%
ValueCountFrequency (%)
3157458
94.9%
43508
 
0.8%
89150
 
0.2%
9319
 
< 0.1%
97367
 
0.6%
11151
 
0.1%
197433
 
0.7%
ValueCountFrequency (%)
197433
 
0.7%
11151
 
0.1%
97367
 
0.6%
9319
 
< 0.1%
89150
 
0.2%
43508
 
0.8%
3157458
94.9%

LANDUSE
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct54
Distinct (%)0.1%
Missing1551
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean2464.537433
Minimum1111
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:45.321991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1111
5-th percentile1111
Q11111
median1130
Q36000
95-th percentile6000
Maximum9999
Range8888
Interquartile range (IQR)4889

Descriptive statistics

Standard deviation2138.37101
Coefficient of variation (CV)0.8676561298
Kurtosis-0.8954046743
Mean2464.537433
Median Absolute Deviation (MAD)19
Skewness1.037615461
Sum145373205
Variance4572630.578
MonotonicityNot monotonic
2022-01-11T10:46:45.374093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113016666
27.5%
600015623
25.8%
111115089
24.9%
13103630
 
6.0%
11121485
 
2.5%
12151411
 
2.3%
12161303
 
2.2%
1250681
 
1.1%
1140316
 
0.5%
1220307
 
0.5%
Other values (44)2475
 
4.1%
(Missing)1551
 
2.6%
ValueCountFrequency (%)
111115089
24.9%
11121485
 
2.5%
113016666
27.5%
1140316
 
0.5%
115154
 
0.1%
121118
 
< 0.1%
121290
 
0.1%
121435
 
0.1%
12151411
 
2.3%
12161303
 
2.2%
ValueCountFrequency (%)
99998
 
< 0.1%
600015623
25.8%
50003
 
< 0.1%
424027
 
< 0.1%
42301
 
< 0.1%
42207
 
< 0.1%
42105
 
< 0.1%
414024
 
< 0.1%
41306
 
< 0.1%
412047
 
0.1%

LANDUSE2
Categorical

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)< 0.1%
Missing17174
Missing (%)28.4%
Memory size2.9 MiB
43305 
1350
 
16
1310
 
10
3200
 
7
1512
 
6
Other values (6)
 
19

Length

Max length9
Median length1
Mean length1.00447386
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
43305
71.5%
135016
 
< 0.1%
131010
 
< 0.1%
32007
 
< 0.1%
15126
 
< 0.1%
12156
 
< 0.1%
11305
 
< 0.1%
1215;12504
 
< 0.1%
15112
 
< 0.1%
12401
 
< 0.1%
(Missing)17174
 
28.4%

Length

2022-01-11T10:46:45.423791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
135016
27.6%
131010
17.2%
32007
12.1%
15126
 
10.3%
12156
 
10.3%
11305
 
8.6%
1215;12504
 
6.9%
15112
 
3.4%
12401
 
1.7%
20001
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

OS_MGMT
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing59313
Missing (%)98.0%
Memory size1.9 MiB
844 
MUNI
283 
CNTY
 
66
XXXX
 
31

Length

Max length4
Median length1
Mean length1.931372549
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
844
 
1.4%
MUNI283
 
0.5%
CNTY66
 
0.1%
XXXX31
 
0.1%
(Missing)59313
98.0%

Length

2022-01-11T10:46:45.467534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:45.496461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
muni283
74.5%
cnty66
 
17.4%
xxxx31
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FAC_NAME
Categorical

HIGH CORRELATION
MISSING

Distinct33
Distinct (%)17.6%
Missing60349
Missing (%)99.7%
Memory size1.9 MiB
O'Hare International Airport
113 
Midway International Airport
 
10
University of Illinois at Chicago
 
7
Loyola University Chicago
 
6
The University of Chicago
 
6
Other values (28)
46 

Length

Max length45
Median length28
Mean length26.74468085
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)10.1%

Sample

1st rowThe University of Chicago
2nd rowUniversity of Illinois at Chicago
3rd rowRandhurst Village
4th rowThe University of Chicago
5th rowWestfield Chicago Ridge

Common Values

ValueCountFrequency (%)
O'Hare International Airport113
 
0.2%
Midway International Airport10
 
< 0.1%
University of Illinois at Chicago7
 
< 0.1%
Loyola University Chicago6
 
< 0.1%
The University of Chicago6
 
< 0.1%
Northwestern University6
 
< 0.1%
Woodfield Mall4
 
< 0.1%
River Oaks Center3
 
< 0.1%
Randhurst Village3
 
< 0.1%
DePaul University3
 
< 0.1%
Other values (23)27
 
< 0.1%
(Missing)60349
99.7%

Length

2022-01-11T10:46:45.533414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
airport123
21.1%
international123
21.1%
o'hare113
19.3%
university34
 
5.8%
chicago23
 
3.9%
of15
 
2.6%
midway10
 
1.7%
illinois10
 
1.7%
college8
 
1.4%
northwestern7
 
1.2%
Other values (61)118
20.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLATTED
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing58171
Missing (%)96.1%
Memory size1.9 MiB
2358 
O
 
7
C
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
2358
 
3.9%
O7
 
< 0.1%
C1
 
< 0.1%
(Missing)58171
96.1%

Length

2022-01-11T10:46:45.574478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:45.599732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
o7
87.5%
c1
 
12.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MODIFIER
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.2%
Missing58163
Missing (%)96.1%
Memory size1.9 MiB
2315 
M
 
54
T
 
3
S
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
2315
 
3.8%
M54
 
0.1%
T3
 
< 0.1%
S2
 
< 0.1%
(Missing)58163
96.1%

Length

2022-01-11T10:46:45.627706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:45.653779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
m54
91.5%
t3
 
5.1%
s2
 
3.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

STATEFP
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1341
Missing (%)2.2%
Memory size3.5 MiB
17.0
59196 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17.0
2nd row17.0
3rd row17.0
4th row17.0
5th row17.0

Common Values

ValueCountFrequency (%)
17.059196
97.8%
(Missing)1341
 
2.2%

Length

2022-01-11T10:46:45.684875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:45.709917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
17.059196
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

COUNTYFP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct56
Distinct (%)0.1%
Missing1341
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean33.20224339
Minimum1
Maximum201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:45.741842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q131
median31
Q331
95-th percentile31
Maximum201
Range200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.27643347
Coefficient of variation (CV)0.4902208948
Kurtosis79.81528719
Mean33.20224339
Median Absolute Deviation (MAD)0
Skewness8.693335236
Sum1965440
Variance264.9222864
MonotonicityNot monotonic
2022-01-11T10:46:45.795607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3157456
94.9%
43507
 
0.8%
197434
 
0.7%
97369
 
0.6%
89150
 
0.2%
9158
 
0.1%
11151
 
0.1%
9319
 
< 0.1%
6316
 
< 0.1%
9912
 
< 0.1%
Other values (46)124
 
0.2%
(Missing)1341
 
2.2%
ValueCountFrequency (%)
11
 
< 0.1%
71
 
< 0.1%
115
 
< 0.1%
151
 
< 0.1%
172
 
< 0.1%
1910
 
< 0.1%
211
 
< 0.1%
291
 
< 0.1%
3157456
94.9%
379
 
< 0.1%
ValueCountFrequency (%)
2019
 
< 0.1%
1991
 
< 0.1%
197434
0.7%
1956
 
< 0.1%
1851
 
< 0.1%
1833
 
< 0.1%
1811
 
< 0.1%
1792
 
< 0.1%
1732
 
< 0.1%
1672
 
< 0.1%

TRACTCE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1937
Distinct (%)3.3%
Missing1341
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean584201.0844
Minimum100
Maximum980100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:45.850353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile40202
Q1291200
median740300
Q3822500
95-th percentile841628.75
Maximum980100
Range980000
Interquartile range (IQR)531300

Descriptive statistics

Standard deviation286468.0643
Coefficient of variation (CV)0.4903586658
Kurtosis-1.066405766
Mean584201.0844
Median Absolute Deviation (MAD)97700
Skewness-0.7086812104
Sum3.45823674 × 1010
Variance8.206395189 × 1010
MonotonicityNot monotonic
2022-01-11T10:46:45.901906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
431400263
 
0.4%
231500257
 
0.4%
839100244
 
0.4%
231200208
 
0.3%
808100184
 
0.3%
10300167
 
0.3%
31200164
 
0.3%
251900161
 
0.3%
838700155
 
0.3%
816100153
 
0.3%
Other values (1927)57240
94.6%
(Missing)1341
 
2.2%
ValueCountFrequency (%)
1002
 
< 0.1%
1021
 
< 0.1%
1031
 
< 0.1%
1051
 
< 0.1%
1061
 
< 0.1%
2003
 
< 0.1%
3003
 
< 0.1%
3011
 
< 0.1%
40010
< 0.1%
4031
 
< 0.1%
ValueCountFrequency (%)
98010025
 
< 0.1%
980000114
0.2%
9771001
 
< 0.1%
9738001
 
< 0.1%
9728001
 
< 0.1%
9715001
 
< 0.1%
9711003
 
< 0.1%
9656001
 
< 0.1%
9651002
 
< 0.1%
9650001
 
< 0.1%

GEOID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1975
Distinct (%)3.3%
Missing1341
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1.703378644 × 1010
Minimum1.70010103 × 1010
Maximum1.72010039 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:45.953604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.70010103 × 1010
5-th percentile1.70310404 × 1010
Q11.70312924 × 1010
median1.70317502 × 1010
Q31.70318227 × 1010
95-th percentile1.70318423 × 1010
Maximum1.72010039 × 1010
Range199993602
Interquartile range (IQR)530301

Descriptive statistics

Standard deviation16309616.53
Coefficient of variation (CV)0.0009574862634
Kurtosis79.67761645
Mean1.703378644 × 1010
Median Absolute Deviation (MAD)89200
Skewness8.683871002
Sum1.008332022 × 1015
Variance2.660035915 × 1014
MonotonicityNot monotonic
2022-01-11T10:46:46.095412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.70314314 × 1010263
 
0.4%
1.70312315 × 1010257
 
0.4%
1.70318391 × 1010244
 
0.4%
1.70312312 × 1010208
 
0.3%
1.70318081 × 1010184
 
0.3%
1.70310103 × 1010165
 
0.3%
1.70310312 × 1010164
 
0.3%
1.70312519 × 1010161
 
0.3%
1.70318387 × 1010155
 
0.3%
1.7031491 × 1010153
 
0.3%
Other values (1965)57242
94.6%
(Missing)1341
 
2.2%
ValueCountFrequency (%)
1.70010103 × 10101
< 0.1%
1.70070106 × 10101
< 0.1%
1.70119649 × 10101
< 0.1%
1.7011965 × 10101
< 0.1%
1.70119651 × 10102
< 0.1%
1.70119656 × 10101
< 0.1%
1.70159605 × 10101
< 0.1%
1.70179601 × 10101
< 0.1%
1.70179602 × 10101
< 0.1%
1.70190003 × 10101
< 0.1%
ValueCountFrequency (%)
1.72010039 × 10101
< 0.1%
1.72010036 × 10101
< 0.1%
1.72010034 × 10101
< 0.1%
1.72010032 × 10101
< 0.1%
1.72010031 × 10101
< 0.1%
1.72010027 × 10101
< 0.1%
1.72010026 × 10101
< 0.1%
1.72010004 × 10101
< 0.1%
1.72010001 × 10101
< 0.1%
1.71990214 × 10101
< 0.1%

NAME
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1937
Distinct (%)3.3%
Missing1341
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean5842.010844
Minimum1
Maximum9801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:46.155419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile402.02
Q12912
median7403
Q38225
95-th percentile8416.2875
Maximum9801
Range9800
Interquartile range (IQR)5313

Descriptive statistics

Standard deviation2864.680643
Coefficient of variation (CV)0.4903586658
Kurtosis-1.066405766
Mean5842.010844
Median Absolute Deviation (MAD)977
Skewness-0.7086812104
Sum345823674
Variance8206395.189
MonotonicityNot monotonic
2022-01-11T10:46:46.209733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4314263
 
0.4%
2315257
 
0.4%
8391244
 
0.4%
2312208
 
0.3%
8081184
 
0.3%
103167
 
0.3%
312164
 
0.3%
2519161
 
0.3%
8387155
 
0.3%
8161153
 
0.3%
Other values (1927)57240
94.6%
(Missing)1341
 
2.2%
ValueCountFrequency (%)
12
 
< 0.1%
1.021
 
< 0.1%
1.031
 
< 0.1%
1.051
 
< 0.1%
1.061
 
< 0.1%
23
 
< 0.1%
33
 
< 0.1%
3.011
 
< 0.1%
410
< 0.1%
4.031
 
< 0.1%
ValueCountFrequency (%)
980125
 
< 0.1%
9800114
0.2%
97711
 
< 0.1%
97381
 
< 0.1%
97281
 
< 0.1%
97151
 
< 0.1%
97113
 
< 0.1%
96561
 
< 0.1%
96512
 
< 0.1%
96501
 
< 0.1%

NAMELSAD
Categorical

HIGH CARDINALITY
MISSING

Distinct1937
Distinct (%)3.3%
Missing1341
Missing (%)2.2%
Memory size4.3 MiB
Census Tract 4314
 
263
Census Tract 2315
 
257
Census Tract 8391
 
244
Census Tract 2312
 
208
Census Tract 8081
 
184
Other values (1932)
58040 

Length

Max length20
Median length17
Mean length17.95930468
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)0.5%

Sample

1st rowCensus Tract 208.01
2nd rowCensus Tract 2519
3rd rowCensus Tract 1001
4th rowCensus Tract 8201.01
5th rowCensus Tract 8230.01

Common Values

ValueCountFrequency (%)
Census Tract 4314263
 
0.4%
Census Tract 2315257
 
0.4%
Census Tract 8391244
 
0.4%
Census Tract 2312208
 
0.3%
Census Tract 8081184
 
0.3%
Census Tract 103167
 
0.3%
Census Tract 312164
 
0.3%
Census Tract 2519161
 
0.3%
Census Tract 8387155
 
0.3%
Census Tract 8161153
 
0.3%
Other values (1927)57240
94.6%
(Missing)1341
 
2.2%

Length

2022-01-11T10:46:46.260369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
census59196
33.3%
tract59196
33.3%
4314263
 
0.1%
2315257
 
0.1%
8391244
 
0.1%
2312208
 
0.1%
8081184
 
0.1%
103167
 
0.1%
312164
 
0.1%
2519161
 
0.1%
Other values (1929)57548
32.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MTFCC
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1341
Missing (%)2.2%
Memory size3.5 MiB
G5020
59196 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG5020
2nd rowG5020
3rd rowG5020
4th rowG5020
5th rowG5020

Common Values

ValueCountFrequency (%)
G502059196
97.8%
(Missing)1341
 
2.2%

Length

2022-01-11T10:46:46.302506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:46.327509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
g502059196
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FUNCSTAT
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1341
Missing (%)2.2%
Memory size3.3 MiB
S
59196 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S59196
97.8%
(Missing)1341
 
2.2%

Length

2022-01-11T10:46:46.354300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:46.379627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
s59196
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ALAND
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct1975
Distinct (%)3.3%
Missing1341
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2618399.872
Minimum22158
Maximum589712667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:46.411449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22158
5-th percentile266386
Q1576183
median1010886
Q32428330.25
95-th percentile7053776
Maximum589712667
Range589690509
Interquartile range (IQR)1852147.25

Descriptive statistics

Standard deviation11696788.37
Coefficient of variation (CV)4.467151291
Kurtosis888.7906674
Mean2618399.872
Median Absolute Deviation (MAD)633878
Skewness26.68689517
Sum1.549987988 × 1011
Variance1.368148581 × 1014
MonotonicityNot monotonic
2022-01-11T10:46:46.464416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1084987263
 
0.4%
999706257
 
0.4%
1101771244
 
0.4%
656674208
 
0.3%
4896434184
 
0.3%
472522165
 
0.3%
326475164
 
0.3%
632452161
 
0.3%
658085155
 
0.3%
1277974153
 
0.3%
Other values (1965)57242
94.6%
(Missing)1341
 
2.2%
ValueCountFrequency (%)
2215823
 
< 0.1%
648275
 
< 0.1%
690947
 
< 0.1%
6987827
< 0.1%
8276911
 
< 0.1%
8663114
 
< 0.1%
9384326
< 0.1%
10299664
0.1%
10648312
 
< 0.1%
10867715
 
< 0.1%
ValueCountFrequency (%)
5897126671
< 0.1%
5409538481
< 0.1%
5199807661
< 0.1%
4815810341
< 0.1%
4771432511
< 0.1%
4364434621
< 0.1%
4272562131
< 0.1%
4215765841
< 0.1%
4196003651
< 0.1%
4144280711
< 0.1%

AWATER
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct731
Distinct (%)1.2%
Missing1341
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean66665.51512
Minimum0
Maximum43826867
Zeros46101
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:46.519717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile283388
Maximum43826867
Range43826867
Interquartile range (IQR)0

Descriptive statistics

Standard deviation420153.81
Coefficient of variation (CV)6.302416013
Kurtosis3056.115878
Mean66665.51512
Median Absolute Deviation (MAD)0
Skewness38.48057344
Sum3946331833
Variance1.76529224 × 1011
MonotonicityNot monotonic
2022-01-11T10:46:46.568399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046101
76.2%
1126154263
 
0.4%
84111244
 
0.4%
143193127
 
0.2%
14139124
 
0.2%
92402114
 
0.2%
143185113
 
0.2%
466785110
 
0.2%
7945105
 
0.2%
68430105
 
0.2%
Other values (721)11790
 
19.5%
(Missing)1341
 
2.2%
ValueCountFrequency (%)
046101
76.2%
651
 
< 0.1%
1391
 
< 0.1%
2623
 
< 0.1%
32465
 
0.1%
5332
 
< 0.1%
56225
 
< 0.1%
5901
 
< 0.1%
6622
 
< 0.1%
8011
 
< 0.1%
ValueCountFrequency (%)
438268671
< 0.1%
351708321
< 0.1%
174360301
< 0.1%
169155182
< 0.1%
168138841
< 0.1%
123417951
< 0.1%
92611221
< 0.1%
90972811
< 0.1%
90690281
< 0.1%
88870041
< 0.1%

INTPTLAT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1975
Distinct (%)3.3%
Missing1341
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean41.8418872
Minimum37.22419
Maximum42.4859826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:46.616869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37.22419
5-th percentile41.5824275
Q141.7482097
median41.8614519
Q341.94680475
95-th percentile42.071667
Maximum42.4859826
Range5.2617926
Interquartile range (IQR)0.19859505

Descriptive statistics

Standard deviation0.1703825957
Coefficient of variation (CV)0.0040720581
Kurtosis91.88341528
Mean41.8418872
Median Absolute Deviation (MAD)0.1002536
Skewness-4.541504298
Sum2476872.355
Variance0.02903022892
MonotonicityNot monotonic
2022-01-11T10:46:46.669266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.7572084263
 
0.4%
41.8917357257
 
0.4%
41.8809857244
 
0.4%
41.8990614208
 
0.3%
42.0113034184
 
0.3%
42.0159431165
 
0.3%
41.9727092164
 
0.3%
41.8838116161
 
0.3%
41.8625775155
 
0.3%
41.6997625153
 
0.3%
Other values (1965)57242
94.6%
(Missing)1341
 
2.2%
ValueCountFrequency (%)
37.224193
< 0.1%
37.38944531
 
< 0.1%
37.66759751
 
< 0.1%
37.66803561
 
< 0.1%
37.69635151
 
< 0.1%
37.72304041
 
< 0.1%
37.93094321
 
< 0.1%
38.30789671
 
< 0.1%
38.43429561
 
< 0.1%
38.44533751
 
< 0.1%
ValueCountFrequency (%)
42.48598261
< 0.1%
42.48571811
< 0.1%
42.47947321
< 0.1%
42.4700912
< 0.1%
42.4613451
< 0.1%
42.45616681
< 0.1%
42.45605061
< 0.1%
42.45525591
< 0.1%
42.45348571
< 0.1%
42.453391
< 0.1%

INTPTLON
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1975
Distinct (%)3.3%
Missing1341
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean-87.73909182
Minimum-91.1590178
Maximum-87.5294325
Zeros0
Zeros (%)0.0%
Negative59196
Negative (%)97.8%
Memory size473.1 KiB
2022-01-11T10:46:46.720429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-91.1590178
5-th percentile-88.0130994
Q1-87.7884717
median-87.7117002
Q3-87.6509155
95-th percentile-87.5804887
Maximum-87.5294325
Range3.6295853
Interquartile range (IQR)0.1375562

Descriptive statistics

Standard deviation0.1478177619
Coefficient of variation (CV)-0.001684742329
Kurtosis49.91620952
Mean-87.73909182
Median Absolute Deviation (MAD)0.06659
Skewness-4.243875324
Sum-5193803.279
Variance0.02185009073
MonotonicityNot monotonic
2022-01-11T10:46:46.772214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.546555263
 
0.4%
-87.7186525257
 
0.4%
-87.6330703244
 
0.4%
-87.7213153208
 
0.3%
-87.7900018184
 
0.3%
-87.6665393165
 
0.3%
-87.657341164
 
0.3%
-87.7600945161
 
0.3%
-87.7201851155
 
0.3%
-87.6280536153
 
0.3%
Other values (1965)57242
94.6%
(Missing)1341
 
2.2%
ValueCountFrequency (%)
-91.15901781
< 0.1%
-90.7112571
< 0.1%
-90.66719491
< 0.1%
-90.63794091
< 0.1%
-90.52898931
< 0.1%
-90.50790841
< 0.1%
-90.42297251
< 0.1%
-90.4169741
< 0.1%
-90.32672441
< 0.1%
-90.23141061
< 0.1%
ValueCountFrequency (%)
-87.529432529
 
< 0.1%
-87.530702924
 
< 0.1%
-87.530871929
 
< 0.1%
-87.532407737
0.1%
-87.534899320
 
< 0.1%
-87.535585431
0.1%
-87.535629928
 
< 0.1%
-87.535978574
0.1%
-87.536365833
0.1%
-87.536725377
0.1%

OBJECTID_12
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct81
Distinct (%)26.9%
Missing60236
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean1657.099668
Minimum64
Maximum2167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:46.826861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile755
Q11122
median1781
Q32145
95-th percentile2167
Maximum2167
Range2103
Interquartile range (IQR)1023

Descriptive statistics

Standard deviation537.868686
Coefficient of variation (CV)0.3245843907
Kurtosis-0.7641775448
Mean1657.099668
Median Absolute Deviation (MAD)365
Skewness-0.7502818647
Sum498787
Variance289302.7234
MonotonicityNot monotonic
2022-01-11T10:46:46.874765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178143
 
0.1%
214541
 
0.1%
214633
 
0.1%
110923
 
< 0.1%
216722
 
< 0.1%
209316
 
< 0.1%
80010
 
< 0.1%
4899
 
< 0.1%
12476
 
< 0.1%
18505
 
< 0.1%
Other values (71)93
 
0.2%
(Missing)60236
99.5%
ValueCountFrequency (%)
641
 
< 0.1%
4441
 
< 0.1%
4881
 
< 0.1%
4899
< 0.1%
5191
 
< 0.1%
7361
 
< 0.1%
7391
 
< 0.1%
7551
 
< 0.1%
7611
 
< 0.1%
7632
 
< 0.1%
ValueCountFrequency (%)
216722
< 0.1%
21493
 
< 0.1%
214633
0.1%
214541
0.1%
21441
 
< 0.1%
21221
 
< 0.1%
21075
 
< 0.1%
21022
 
< 0.1%
20971
 
< 0.1%
209316
 
< 0.1%

CFNAME
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct77
Distinct (%)25.6%
Missing60236
Missing (%)99.5%
Memory size1.9 MiB
Lincoln (Abraham) Park
43 
Jackson Park
42 
Burnham Park
33 
Humboldt Park
23 
Grant Park
22 
Other values (72)
138 

Length

Max length30
Median length12
Mean length14.66777409
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)17.9%

Sample

1st rowAltman Park
2nd rowJackson Park
3rd rowAltgeld Park
4th rowJackson Park
5th rowSeward Park

Common Values

ValueCountFrequency (%)
Lincoln (Abraham) Park43
 
0.1%
Jackson Park42
 
0.1%
Burnham Park33
 
0.1%
Humboldt Park23
 
< 0.1%
Grant Park22
 
< 0.1%
Garfield Park16
 
< 0.1%
Washington Park11
 
< 0.1%
Douglas Park10
 
< 0.1%
Lincolnwood Centennial Park9
 
< 0.1%
Loyola Park5
 
< 0.1%
Other values (67)87
 
0.1%
(Missing)60236
99.5%

Length

2022-01-11T10:46:46.919825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
park292
43.0%
lincoln43
 
6.3%
abraham43
 
6.3%
jackson42
 
6.2%
burnham33
 
4.9%
humboldt23
 
3.4%
grant22
 
3.2%
garfield16
 
2.4%
washington11
 
1.6%
centennial10
 
1.5%
Other values (93)144
21.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CFTYPE
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.3%
Missing60236
Missing (%)99.5%
Memory size1.9 MiB
Park
301 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPark
2nd rowPark
3rd rowPark
4th rowPark
5th rowPark

Common Values

ValueCountFrequency (%)
Park301
 
0.5%
(Missing)60236
99.5%

Length

2022-01-11T10:46:46.958156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:46.981605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
park301
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CFSUBTYPE
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.3%
Missing60236
Missing (%)99.5%
Memory size1.9 MiB
Park
301 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPark
2nd rowPark
3rd rowPark
4th rowPark
5th rowPark

Common Values

ValueCountFrequency (%)
Park301
 
0.5%
(Missing)60236
99.5%

Length

2022-01-11T10:46:47.006201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:47.029974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
park301
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ADDRESS
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)24.3%
Missing60236
Missing (%)99.5%
Memory size1.9 MiB
200 W Fullerton Pkwy
43 
6401 S Stony Island Ave
42 
425 E Mcfetridge Dr
33 
1400 N Humboldt Dr
23 
331 E Randolph St
22 
Other values (68)
138 

Length

Max length34
Median length19
Mean length18.65780731
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)16.9%

Sample

1st row7205 115th St
2nd row6401 S Stony Island Ave
3rd row531 N Washtenaw Ave
4th row6401 S Stony Island Ave
5th row375 W Elm St

Common Values

ValueCountFrequency (%)
200 W Fullerton Pkwy43
 
0.1%
6401 S Stony Island Ave42
 
0.1%
425 E Mcfetridge Dr33
 
0.1%
1400 N Humboldt Dr23
 
< 0.1%
331 E Randolph St22
 
< 0.1%
Unknown19
 
< 0.1%
100 N Central Park Dr16
 
< 0.1%
1613 S Sacramento Dr10
 
< 0.1%
5531 S Dr Martin Luther King Jr Dr6
 
< 0.1%
1230 W Greenleaf Ave5
 
< 0.1%
Other values (63)82
 
0.1%
(Missing)60236
99.5%

Length

2022-01-11T10:46:47.057628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dr101
 
8.2%
s88
 
7.2%
ave86
 
7.0%
w65
 
5.3%
e61
 
5.0%
n50
 
4.1%
fullerton44
 
3.6%
20043
 
3.5%
pkwy43
 
3.5%
island42
 
3.4%
Other values (148)604
49.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GNISCODE
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct66
Distinct (%)21.9%
Missing60236
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean832189.9934
Minimum0
Maximum2026789
Zeros30
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:47.190389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1409294
median411001
Q31829853
95-th percentile1832764
Maximum2026789
Range2026789
Interquartile range (IQR)1420559

Descriptive statistics

Standard deviation704422.5875
Coefficient of variation (CV)0.8464684665
Kurtosis-1.412604314
Mean832189.9934
Median Absolute Deviation (MAD)7365
Skewness0.6452236989
Sum250489188
Variance4.962111818 × 1011
MonotonicityNot monotonic
2022-01-11T10:46:47.246046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182985343
 
0.1%
41100142
 
0.1%
183276433
 
0.1%
030
 
< 0.1%
41067923
 
< 0.1%
40929422
 
< 0.1%
40889316
 
< 0.1%
40734110
 
< 0.1%
4205676
 
< 0.1%
4127325
 
< 0.1%
Other values (56)71
 
0.1%
(Missing)60236
99.5%
ValueCountFrequency (%)
030
< 0.1%
4034663
 
< 0.1%
4037291
 
< 0.1%
4041821
 
< 0.1%
4053761
 
< 0.1%
4061641
 
< 0.1%
4064182
 
< 0.1%
40734110
 
< 0.1%
4076901
 
< 0.1%
4078441
 
< 0.1%
ValueCountFrequency (%)
20267891
 
< 0.1%
18664671
 
< 0.1%
18664561
 
< 0.1%
18371271
 
< 0.1%
18367671
 
< 0.1%
18366531
 
< 0.1%
18329751
 
< 0.1%
18329641
 
< 0.1%
18328431
 
< 0.1%
183276433
0.1%

COMMENT
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)1.0%
Missing60236
Missing (%)99.5%
Memory size1.9 MiB
243 
Multiple additional GNIS codes.
55 
Additional GNIS code of 1831526.
 
3

Length

Max length32
Median length1
Mean length6.790697674
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
243
 
0.4%
Multiple additional GNIS codes.55
 
0.1%
Additional GNIS code of 1831526.3
 
< 0.1%
(Missing)60236
99.5%

Length

2022-01-11T10:46:47.300620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:47.328913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
additional58
24.7%
gnis58
24.7%
multiple55
23.4%
codes55
23.4%
code3
 
1.3%
of3
 
1.3%
18315263
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SOURCE
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)2.7%
Missing60236
Missing (%)99.5%
Memory size1.9 MiB
CGIL
155 
CGI
102 
GI
20 
CG
 
12
GIL
 
9
Other values (3)
 
3

Length

Max length4
Median length4
Mean length3.398671096
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.0%

Sample

1st rowCG
2nd rowCGIL
3rd rowCGIL
4th rowCGIL
5th rowCGI

Common Values

ValueCountFrequency (%)
CGIL155
 
0.3%
CGI102
 
0.2%
GI20
 
< 0.1%
CG12
 
< 0.1%
GIL9
 
< 0.1%
CGL1
 
< 0.1%
G1
 
< 0.1%
CI1
 
< 0.1%
(Missing)60236
99.5%

Length

2022-01-11T10:46:47.366893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:47.400216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
cgil155
51.5%
cgi102
33.9%
gi20
 
6.6%
cg12
 
4.0%
gil9
 
3.0%
cgl1
 
0.3%
g1
 
0.3%
ci1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Jurisdicti
Categorical

HIGH CORRELATION
MISSING

Distinct28
Distinct (%)9.3%
Missing60236
Missing (%)99.5%
Memory size1.9 MiB
CHICAGO PARK DISTRICT
231 
METRO WATER RECLM DIST
 
16
 
15
IL DEPT-MILITARY AFFRS
 
6
NORTHLAKE CITY OF
 
4
Other values (23)
29 

Length

Max length22
Median length21
Mean length19.67774086
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)6.0%

Sample

1st rowMETRO WATER RECLM DIST
2nd rowCHICAGO PARK DISTRICT
3rd rowCHICAGO PARK DISTRICT
4th rowCHICAGO PARK DISTRICT
5th rowCHICAGO PARK DISTRICT

Common Values

ValueCountFrequency (%)
CHICAGO PARK DISTRICT231
 
0.4%
METRO WATER RECLM DIST16
 
< 0.1%
15
 
< 0.1%
IL DEPT-MILITARY AFFRS6
 
< 0.1%
NORTHLAKE CITY OF4
 
< 0.1%
BURBANK PARK DIST3
 
< 0.1%
ALSIP PARK DIST2
 
< 0.1%
STATE OF ILLINOIS2
 
< 0.1%
CITY OF CHICAGO2
 
< 0.1%
WILMETTE PARK DIST2
 
< 0.1%
Other values (18)18
 
< 0.1%
(Missing)60236
99.5%

Length

2022-01-11T10:46:47.442538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
park251
28.6%
chicago234
26.7%
district231
26.3%
dist37
 
4.2%
metro16
 
1.8%
water16
 
1.8%
reclm16
 
1.8%
of11
 
1.3%
city7
 
0.8%
il6
 
0.7%
Other values (32)53
 
6.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Community
Categorical

HIGH CORRELATION
MISSING

Distinct22
Distinct (%)7.3%
Missing60236
Missing (%)99.5%
Memory size1.9 MiB
Chicago
259 
Lincolnwood
 
9
Northlake
 
4
Worth
 
4
Skokie
 
3
Other values (17)
 
22

Length

Max length15
Median length7
Mean length7.202657807
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)4.0%

Sample

1st rowWorth
2nd rowChicago
3rd rowChicago
4th rowChicago
5th rowChicago

Common Values

ValueCountFrequency (%)
Chicago259
 
0.4%
Lincolnwood9
 
< 0.1%
Northlake4
 
< 0.1%
Worth4
 
< 0.1%
Skokie3
 
< 0.1%
Burbank2
 
< 0.1%
Wilmette2
 
< 0.1%
Cicero2
 
< 0.1%
Glenview2
 
< 0.1%
Alsip2
 
< 0.1%
Other values (12)12
 
< 0.1%
(Missing)60236
99.5%

Length

2022-01-11T10:46:47.484210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago259
84.9%
lincolnwood9
 
3.0%
northlake4
 
1.3%
worth4
 
1.3%
skokie3
 
1.0%
cicero2
 
0.7%
alsip2
 
0.7%
glenview2
 
0.7%
wilmette2
 
0.7%
burbank2
 
0.7%
Other values (16)16
 
5.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

landuse_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct54
Distinct (%)0.1%
Missing1551
Missing (%)2.6%
Memory size4.2 MiB
Multi-Family
16666 
NON-PARCEL AREAS
15623 
Single-Family Detached
15089 
Medical Facilities
3630 
Single-Family Attached
 
1485
Other values (49)
6493 

Length

Max length54
Median length16
Mean length17.26360492
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMulti-Family
2nd rowSingle-Family Detached
3rd rowSingle-Family Detached
4th rowNON-PARCEL AREAS
5th rowMulti-Family

Common Values

ValueCountFrequency (%)
Multi-Family16666
27.5%
NON-PARCEL AREAS15623
25.8%
Single-Family Detached15089
24.9%
Medical Facilities3630
 
6.0%
Single-Family Attached1485
 
2.5%
Urban Mix1411
 
2.3%
Urban Mix w/Residential Component1303
 
2.2%
Hotel/Motel681
 
1.1%
Mobile Home Parks and Trailer Courts316
 
0.5%
Office307
 
0.5%
Other values (44)2475
 
4.1%
(Missing)1551
 
2.6%

Length

2022-01-11T10:46:47.534218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
multi-family16666
15.5%
single-family16574
15.4%
areas15623
14.6%
non-parcel15623
14.6%
detached15089
14.1%
facilities3977
 
3.7%
medical3630
 
3.4%
urban2714
 
2.5%
mix2714
 
2.5%
attached1485
 
1.4%
Other values (89)13200
12.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

landuse_sub_name
Categorical

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)< 0.1%
Missing1551
Missing (%)2.6%
Memory size4.0 MiB
Residential
33610 
Non-Parcel Areas
15623 
Institutional
4153 
Commercial
3928 
TRANS/COMM/UTIL/WASTE
 
661
Other values (10)
 
1011

Length

Max length23
Median length11
Mean length12.59066558
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResidential
2nd rowResidential
3rd rowResidential
4th rowNon-Parcel Areas
5th rowResidential

Common Values

ValueCountFrequency (%)
Residential33610
55.5%
Non-Parcel Areas15623
25.8%
Institutional4153
 
6.9%
Commercial3928
 
6.5%
TRANS/COMM/UTIL/WASTE661
 
1.1%
Industrial397
 
0.7%
Primarily Recreation266
 
0.4%
Vacant/Undeveloped Land135
 
0.2%
Primarily Conservation65
 
0.1%
Golf Course49
 
0.1%
Other values (5)99
 
0.2%
(Missing)1551
 
2.6%

Length

2022-01-11T10:46:47.586972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
residential33610
44.7%
non-parcel15623
20.8%
areas15623
20.8%
institutional4153
 
5.5%
commercial3928
 
5.2%
trans/comm/util/waste661
 
0.9%
industrial397
 
0.5%
primarily331
 
0.4%
recreation266
 
0.4%
vacant/undeveloped135
 
0.2%
Other values (13)475
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

landuse_major_name
Categorical

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)< 0.1%
Missing1551
Missing (%)2.6%
Memory size3.9 MiB
Urbanized
42749 
Non-Parcel Areas
15623 
Open Space
 
395
Vacant/Under Construction
 
175
AGRICULTURE
 
33
Other values (2)
 
11

Length

Max length25
Median length9
Mean length10.91004645
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrbanized
2nd rowUrbanized
3rd rowUrbanized
4th rowNon-Parcel Areas
5th rowUrbanized

Common Values

ValueCountFrequency (%)
Urbanized42749
70.6%
Non-Parcel Areas15623
 
25.8%
Open Space395
 
0.7%
Vacant/Under Construction175
 
0.3%
AGRICULTURE33
 
0.1%
Not Classifiable8
 
< 0.1%
Water3
 
< 0.1%
(Missing)1551
 
2.6%

Length

2022-01-11T10:46:47.635471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:47.668018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
urbanized42749
56.9%
non-parcel15623
 
20.8%
areas15623
 
20.8%
open395
 
0.5%
space395
 
0.5%
vacant/under175
 
0.2%
construction175
 
0.2%
agriculture33
 
< 0.1%
not8
 
< 0.1%
classifiable8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_datetime
Categorical

HIGH CARDINALITY
UNIFORM

Distinct59410
Distinct (%)98.2%
Missing59
Missing (%)0.1%
Memory size4.4 MiB
2018-08-26 04:09:00
 
7
2016-02-04 13:53:00
 
6
2019-04-19 10:45:00
 
5
2017-03-30 16:32:00
 
4
2016-08-23 06:45:00
 
3
Other values (59405)
60453 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58394 ?
Unique (%)96.6%

Sample

1st row2021-12-14 20:15:00
2nd row2021-12-15 03:16:00
3rd row2021-12-15 00:29:00
4th row2021-12-15 00:52:00
5th row2021-12-15 00:40:00

Common Values

ValueCountFrequency (%)
2018-08-26 04:09:007
 
< 0.1%
2016-02-04 13:53:006
 
< 0.1%
2019-04-19 10:45:005
 
< 0.1%
2017-03-30 16:32:004
 
< 0.1%
2016-08-23 06:45:003
 
< 0.1%
2014-10-13 00:30:003
 
< 0.1%
2020-05-14 03:15:003
 
< 0.1%
2016-02-09 01:05:003
 
< 0.1%
2021-03-29 02:15:003
 
< 0.1%
2020-04-20 06:15:003
 
< 0.1%
Other values (59400)60438
99.8%
(Missing)59
 
0.1%

Length

2022-01-11T10:46:47.712857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00242
 
0.2%
12:30:00137
 
0.1%
17:00:00126
 
0.1%
13:30:00124
 
0.1%
14:00:00124
 
0.1%
11:30:00123
 
0.1%
16:00:00123
 
0.1%
15:30:00122
 
0.1%
16:45:00119
 
0.1%
17:30:00118
 
0.1%
Other values (4129)119598
98.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_time
Categorical

HIGH CARDINALITY

Distinct1440
Distinct (%)2.4%
Missing59
Missing (%)0.1%
Memory size3.8 MiB
00:00:00
 
242
12:30:00
 
137
17:00:00
 
126
14:00:00
 
124
13:30:00
 
124
Other values (1435)
59725 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20:15:00
2nd row03:16:00
3rd row00:29:00
4th row00:52:00
5th row00:40:00

Common Values

ValueCountFrequency (%)
00:00:00242
 
0.4%
12:30:00137
 
0.2%
17:00:00126
 
0.2%
14:00:00124
 
0.2%
13:30:00124
 
0.2%
11:30:00123
 
0.2%
16:00:00123
 
0.2%
15:30:00122
 
0.2%
16:45:00119
 
0.2%
17:30:00118
 
0.2%
Other values (1430)59120
97.7%

Length

2022-01-11T10:46:47.754143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00242
 
0.4%
12:30:00137
 
0.2%
17:00:00126
 
0.2%
13:30:00124
 
0.2%
14:00:00124
 
0.2%
11:30:00123
 
0.2%
16:00:00123
 
0.2%
15:30:00122
 
0.2%
16:45:00119
 
0.2%
17:30:00118
 
0.2%
Other values (1430)59120
97.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_year
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing59
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2018.479216
Minimum2008
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:47.790506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2015
Q12017
median2019
Q32020
95-th percentile2021
Maximum2021
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.147931879
Coefficient of variation (CV)0.001064133761
Kurtosis-1.018777488
Mean2018.479216
Median Absolute Deviation (MAD)2
Skewness-0.5144651379
Sum122073586
Variance4.613611356
MonotonicityNot monotonic
2022-01-11T10:46:47.833458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
202016075
26.6%
202111669
19.3%
20166316
 
10.4%
20196272
 
10.4%
20186189
 
10.2%
20176126
 
10.1%
20155650
 
9.3%
20142175
 
3.6%
20133
 
< 0.1%
20112
 
< 0.1%
(Missing)59
 
0.1%
ValueCountFrequency (%)
20081
 
< 0.1%
20112
 
< 0.1%
20133
 
< 0.1%
20142175
 
3.6%
20155650
 
9.3%
20166316
 
10.4%
20176126
 
10.1%
20186189
 
10.2%
20196272
 
10.4%
202016075
26.6%
ValueCountFrequency (%)
202111669
19.3%
202016075
26.6%
20196272
 
10.4%
20186189
 
10.2%
20176126
 
10.1%
20166316
 
10.4%
20155650
 
9.3%
20142175
 
3.6%
20133
 
< 0.1%
20112
 
< 0.1%

death_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing59
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.716442343
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:47.875953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.466717042
Coefficient of variation (CV)0.5161537709
Kurtosis-1.21126643
Mean6.716442343
Median Absolute Deviation (MAD)3
Skewness-0.03173276525
Sum406197
Variance12.01812705
MonotonicityNot monotonic
2022-01-11T10:46:47.913223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
56373
10.5%
126061
10.0%
115775
9.5%
45752
9.5%
104943
8.2%
94899
8.1%
64858
8.0%
14819
8.0%
84569
7.5%
74415
7.3%
Other values (2)8014
13.2%
ValueCountFrequency (%)
14819
8.0%
23920
6.5%
34094
6.8%
45752
9.5%
56373
10.5%
64858
8.0%
74415
7.3%
84569
7.5%
94899
8.1%
104943
8.2%
ValueCountFrequency (%)
126061
10.0%
115775
9.5%
104943
8.2%
94899
8.1%
84569
7.5%
74415
7.3%
64858
8.0%
56373
10.5%
45752
9.5%
34094
6.8%

death_day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.1%
Missing59
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean15.52801019
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:47.955194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.853164683
Coefficient of variation (CV)0.5701416072
Kurtosis-1.2055705
Mean15.52801019
Median Absolute Deviation (MAD)8
Skewness0.0311838786
Sum939103
Variance78.37852491
MonotonicityNot monotonic
2022-01-11T10:46:47.998396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
32148
 
3.5%
22136
 
3.5%
42114
 
3.5%
12085
 
3.4%
82041
 
3.4%
52033
 
3.4%
62022
 
3.3%
142016
 
3.3%
192010
 
3.3%
231999
 
3.3%
Other values (21)39874
65.9%
ValueCountFrequency (%)
12085
3.4%
22136
3.5%
32148
3.5%
42114
3.5%
52033
3.4%
62022
3.3%
71979
3.3%
82041
3.4%
91991
3.3%
101964
3.2%
ValueCountFrequency (%)
311172
1.9%
301788
3.0%
291829
3.0%
281894
3.1%
271907
3.2%
261939
3.2%
251971
3.3%
241998
3.3%
231999
3.3%
221920
3.2%

death_week
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)0.1%
Missing59
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.58267469
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size473.1 KiB
2022-01-11T10:46:48.047401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q115
median27
Q341
95-th percentile51
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.17775259
Coefficient of variation (CV)0.5502639884
Kurtosis-1.196632954
Mean27.58267469
Median Absolute Deviation (MAD)13
Skewness-0.02534844982
Sum1668145
Variance230.3641736
MonotonicityNot monotonic
2022-01-11T10:46:48.102028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
491568
 
2.6%
181552
 
2.6%
191523
 
2.5%
201460
 
2.4%
471458
 
2.4%
481438
 
2.4%
161388
 
2.3%
171387
 
2.3%
211334
 
2.2%
221319
 
2.2%
Other values (43)46051
76.1%
ValueCountFrequency (%)
11168
1.9%
21088
1.8%
31049
1.7%
4996
1.6%
51001
1.7%
6980
1.6%
7923
1.5%
8964
1.6%
9933
1.5%
10936
1.5%
ValueCountFrequency (%)
53604
 
1.0%
521275
2.1%
511225
2.0%
501283
2.1%
491568
2.6%
481438
2.4%
471458
2.4%
461280
2.1%
451236
2.0%
441196
2.0%

motel
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing53642
Missing (%)88.6%
Memory size2.4 MiB
0.0
6815 
1.0
 
80

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06815
 
11.3%
1.080
 
0.1%
(Missing)53642
88.6%

Length

2022-01-11T10:46:48.151375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:48.178522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.06815
98.8%
1.080
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hot_combined
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
0
60490 
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
060490
99.9%
147
 
0.1%

Length

2022-01-11T10:46:48.207297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:48.235003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
060490
99.9%
147
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cold_combined
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
0
60143 
1
 
394

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
060143
99.3%
1394
 
0.7%

Length

2022-01-11T10:46:48.263830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:48.376464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
060143
99.3%
1394
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

primary_combined
Categorical

HIGH CARDINALITY
MISSING

Distinct12238
Distinct (%)20.6%
Missing1045
Missing (%)1.7%
Memory size5.7 MiB
ORGANIC CARDIOVASCULAR DISEASE
5447 
PNEUMONIANOVEL CORONA (COVID-19) VIRAL INFECTION
 
3159
MULTIPLE GUNSHOT WOUNDS
 
3054
HYPERTENSIVE CARDIOVASCULAR DISEASE
 
2514
NOVEL CORONA (COVID-19) VIRAL INFECTION
 
1904
Other values (12233)
43414 

Length

Max length262
Median length39
Mean length42.85697237
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9847 ?
Unique (%)16.6%

Sample

1st rowATHEROSCLEROTIC CARDIOVASCULAR DISEASE
2nd rowORGANIC CARDIOVASCULAR DISEASE
3rd rowORGANIC CARDIOVASCULAR DISEASE
4th rowMULTIPLE INJURIESSPORTS UTILITY VEHICLE STRIKING PEDESTRIAN
5th rowMULTIPLE BLUNT FORCE INJURIESMOTOR VEHICLE COLLISION

Common Values

ValueCountFrequency (%)
ORGANIC CARDIOVASCULAR DISEASE5447
 
9.0%
PNEUMONIANOVEL CORONA (COVID-19) VIRAL INFECTION3159
 
5.2%
MULTIPLE GUNSHOT WOUNDS3054
 
5.0%
HYPERTENSIVE CARDIOVASCULAR DISEASE2514
 
4.2%
NOVEL CORONA (COVID-19) VIRAL INFECTION1904
 
3.1%
HYPERTENSIVE ARTERIOSCLEROTIC CARDIOVASCULAR DISEASE1385
 
2.3%
ARTERIOSCLEROTIC CARDIOVASCULAR DISEASE1174
 
1.9%
PNEUMONIANOVEL CORONA (COVID-19) VIRUS INFECTION908
 
1.5%
GUNSHOT WOUND OF HEAD875
 
1.4%
HANGING736
 
1.2%
Other values (12228)38336
63.3%
(Missing)1045
 
1.7%

Length

2022-01-11T10:46:48.414970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
disease14451
 
5.2%
cardiovascular13802
 
4.9%
covid-1911655
 
4.2%
corona11633
 
4.2%
infection11457
 
4.1%
and9797
 
3.5%
toxicity9444
 
3.4%
fentanyl9437
 
3.4%
of9248
 
3.3%
viral8787
 
3.1%
Other values (4510)169832
60.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

matching_addresses
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
0
60154 
1
 
383

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
060154
99.4%
1383
 
0.6%

Length

2022-01-11T10:46:48.460590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:48.484989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
060154
99.4%
1383
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

repeated_address
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
1
54225 
0
6312 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
154225
89.6%
06312
 
10.4%

Length

2022-01-11T10:46:48.511443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:48.536549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
154225
89.6%
06312
 
10.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

repeated_lat_long
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
1
55568 
0
 
4969

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
155568
91.8%
04969
 
8.2%

Length

2022-01-11T10:46:48.563246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:48.587639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
155568
91.8%
04969
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

incident_date_y
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct52186
Distinct (%)88.4%
Missing1490
Missing (%)2.5%
Memory size4.3 MiB
05/04/2020 12:00 AM
 
34
04/16/2020 12:00 AM
 
34
04/29/2020 12:00 AM
 
33
04/19/2020 12:00 AM
 
32
05/05/2020 12:00 AM
 
32
Other values (52181)
58882 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49291 ?
Unique (%)83.5%

Sample

1st row12/13/2021 04:00 PM
2nd row12/15/2021 02:55 AM
3rd row12/07/2021 09:38 AM
4th row12/14/2021 08:00 PM
5th row12/15/2021 12:01 AM

Common Values

ValueCountFrequency (%)
05/04/2020 12:00 AM34
 
0.1%
04/16/2020 12:00 AM34
 
0.1%
04/29/2020 12:00 AM33
 
0.1%
04/19/2020 12:00 AM32
 
0.1%
05/05/2020 12:00 AM32
 
0.1%
04/24/2020 12:00 AM32
 
0.1%
04/20/2020 12:00 AM32
 
0.1%
04/23/2020 12:00 AM30
 
< 0.1%
12/14/2020 12:00 AM29
 
< 0.1%
04/28/2020 12:00 AM28
 
< 0.1%
Other values (52176)58731
97.0%
(Missing)1490
 
2.5%

Length

2022-01-11T10:46:48.616616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pm31813
 
18.0%
am27234
 
15.4%
12:006763
 
3.8%
10:001234
 
0.7%
03:001080
 
0.6%
08:00864
 
0.5%
09:00817
 
0.5%
07:00732
 
0.4%
01:00730
 
0.4%
11:00729
 
0.4%
Other values (3570)105145
59.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_date_y
Categorical

HIGH CARDINALITY
UNIFORM

Distinct59404
Distinct (%)98.2%
Missing65
Missing (%)0.1%
Memory size4.4 MiB
08/26/2018 04:09 AM
 
7
02/04/2016 01:53 PM
 
6
04/19/2019 10:45 AM
 
5
03/30/2017 04:32 PM
 
4
08/01/2017 12:00 AM
 
3
Other values (59399)
60447 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58388 ?
Unique (%)96.6%

Sample

1st row12/14/2021 08:15 PM
2nd row12/15/2021 03:16 AM
3rd row12/15/2021 12:29 AM
4th row12/15/2021 12:52 AM
5th row12/15/2021 12:40 AM

Common Values

ValueCountFrequency (%)
08/26/2018 04:09 AM7
 
< 0.1%
02/04/2016 01:53 PM6
 
< 0.1%
04/19/2019 10:45 AM5
 
< 0.1%
03/30/2017 04:32 PM4
 
< 0.1%
08/01/2017 12:00 AM3
 
< 0.1%
05/14/2020 11:50 PM3
 
< 0.1%
05/23/2020 03:10 AM3
 
< 0.1%
04/30/2020 12:45 PM3
 
< 0.1%
10/30/2015 05:34 PM3
 
< 0.1%
09/09/2017 11:59 PM3
 
< 0.1%
Other values (59394)60432
99.8%
(Missing)65
 
0.1%

Length

2022-01-11T10:46:48.658100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pm35052
 
19.3%
am25420
 
14.0%
12:00354
 
0.2%
12:30215
 
0.1%
09:30214
 
0.1%
10:30201
 
0.1%
11:30195
 
0.1%
02:00190
 
0.1%
03:30189
 
0.1%
01:30189
 
0.1%
Other values (3406)119197
65.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_street
Categorical

HIGH CARDINALITY
MISSING

Distinct39810
Distinct (%)67.9%
Missing1867
Missing (%)3.1%
Memory size4.4 MiB
4440 West 95th Street
 
830
1775 Dempster Street
 
693
5841 South Maryland Avenue
 
652
2100 Pfingsten Road
 
521
2160 South 1st Avenue
 
463
Other values (39805)
55511 

Length

Max length50
Median length21
Mean length21.4255156
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38224 ?
Unique (%)65.2%

Sample

1st row5145 North California Avenue
2nd row334 N Lorel Ave., 2nd Floor
3rd row7435 West Talcott Avenue
4th row2160 South 1st Avenue
5th rowAdvocate Christ Medical Center 4440 W 95th St.

Common Values

ValueCountFrequency (%)
4440 West 95th Street830
 
1.4%
1775 Dempster Street693
 
1.1%
5841 South Maryland Avenue652
 
1.1%
2100 Pfingsten Road521
 
0.9%
2160 South 1st Avenue463
 
0.8%
1 Ingalls Drive 419
 
0.7%
1500 South Fairfield Avenue 402
 
0.7%
12251 South 80th Avenue392
 
0.6%
7435 West Talcott Avenue362
 
0.6%
4440 W 95TH ST362
 
0.6%
Other values (39800)53574
88.5%
(Missing)1867
 
3.1%

Length

2022-01-11T10:46:48.712446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
avenue12094
 
5.1%
s10367
 
4.3%
w8780
 
3.7%
street8616
 
3.6%
west7420
 
3.1%
south7342
 
3.1%
ave6068
 
2.5%
n5021
 
2.1%
apt4330
 
1.8%
st3660
 
1.5%
Other values (15026)164756
69.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_city
Categorical

HIGH CARDINALITY
MISSING

Distinct293
Distinct (%)0.5%
Missing2173
Missing (%)3.6%
Memory size3.7 MiB
Chicago
33611 
Oak Lawn
 
2272
Maywood
 
1210
Park Ridge
 
1021
Evanston
 
999
Other values (288)
19251 

Length

Max length22
Median length7
Mean length8.222551573
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)0.1%

Sample

1st rowChicago
2nd rowChicago
3rd rowChicago
4th rowMaywood
5th rowOak Lawn

Common Values

ValueCountFrequency (%)
Chicago33611
55.5%
Oak Lawn2272
 
3.8%
Maywood1210
 
2.0%
Park Ridge1021
 
1.7%
Evanston999
 
1.7%
Arlington Heights858
 
1.4%
Glenview811
 
1.3%
Harvey802
 
1.3%
Berwyn788
 
1.3%
Oak Park762
 
1.3%
Other values (283)15230
25.2%
(Missing)2173
 
3.6%

Length

2022-01-11T10:46:48.770216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago34137
46.7%
park4332
 
5.9%
oak3179
 
4.4%
lawn2273
 
3.1%
heights2047
 
2.8%
maywood1210
 
1.7%
ridge1125
 
1.5%
evanston1000
 
1.4%
palos864
 
1.2%
grove863
 
1.2%
Other values (264)22018
30.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_county
Categorical

HIGH CORRELATION
MISSING

Distinct17
Distinct (%)0.1%
Missing27597
Missing (%)45.6%
Memory size3.0 MiB
Cook County
32791 
Du Page County
 
79
Will County
 
17
Lake County
 
16
Lake County/Indiana
 
10
Other values (12)
 
27

Length

Max length19
Median length11
Mean length11.00768063
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowCook County
2nd rowCook County
3rd rowCook County
4th rowCook County
5th rowCook County

Common Values

ValueCountFrequency (%)
Cook County32791
54.2%
Du Page County79
 
0.1%
Will County17
 
< 0.1%
Lake County16
 
< 0.1%
Lake County/Indiana10
 
< 0.1%
Kane County6
 
< 0.1%
Cook6
 
< 0.1%
McHenry County3
 
< 0.1%
Ogle2
 
< 0.1%
Kendall2
 
< 0.1%
Other values (7)8
 
< 0.1%
(Missing)27597
45.6%

Length

2022-01-11T10:46:48.822011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
county32915
49.9%
cook32797
49.7%
du79
 
0.1%
page79
 
0.1%
lake26
 
< 0.1%
will17
 
< 0.1%
county/indiana10
 
< 0.1%
kane6
 
< 0.1%
mchenry3
 
< 0.1%
la2
 
< 0.1%
Other values (9)12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_state
Categorical

HIGH CORRELATION
MISSING

Distinct9
Distinct (%)< 0.1%
Missing2362
Missing (%)3.9%
Memory size4.1 MiB
IL
58099 
IN
 
61
GA
 
7
WI
 
2
MI
 
2
Other values (4)
 
4

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowIL
2nd rowIL
3rd rowIL
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
IL 58099
96.0%
IN 61
 
0.1%
GA 7
 
< 0.1%
WI 2
 
< 0.1%
MI 2
 
< 0.1%
IA 1
 
< 0.1%
TX 1
 
< 0.1%
MN 1
 
< 0.1%
OK 1
 
< 0.1%
(Missing)2362
 
3.9%

Length

2022-01-11T10:46:48.864096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:48.893003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
il58099
99.9%
in61
 
0.1%
ga7
 
< 0.1%
wi2
 
< 0.1%
mi2
 
< 0.1%
ia1
 
< 0.1%
tx1
 
< 0.1%
mn1
 
< 0.1%
ok1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_zip
Categorical

HIGH CARDINALITY
MISSING

Distinct497
Distinct (%)0.9%
Missing3285
Missing (%)5.4%
Memory size3.5 MiB
60612
 
1853
60637
 
1511
60453
 
1461
60608
 
1367
60628
 
1340
Other values (492)
49720 

Length

Max length10
Median length5
Mean length4.999580801
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique163 ?
Unique (%)0.3%

Sample

1st row60659
2nd row60644
3rd row60631
4th row60453
5th row60619

Common Values

ValueCountFrequency (%)
606121853
 
3.1%
606371511
 
2.5%
604531461
 
2.4%
606081367
 
2.3%
606281340
 
2.2%
606171210
 
2.0%
606291202
 
2.0%
606231156
 
1.9%
606241134
 
1.9%
606441116
 
1.8%
Other values (487)43902
72.5%
(Missing)3285
 
5.4%

Length

2022-01-11T10:46:48.936716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
606121853
 
3.2%
606371511
 
2.6%
604531461
 
2.6%
606081367
 
2.4%
606281340
 
2.3%
606171210
 
2.1%
606291202
 
2.1%
606231156
 
2.0%
606241134
 
2.0%
606441116
 
1.9%
Other values (487)43902
76.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_location
Categorical

HIGH CARDINALITY
MISSING

Distinct5808
Distinct (%)16.1%
Missing24546
Missing (%)40.5%
Memory size3.2 MiB
RESIDENCE
 
2516
Residence
 
2320
HOSPITAL ICU
 
1264
HOSPITAL
 
1249
ER
 
1149
Other values (5803)
27493 

Length

Max length248
Median length10
Mean length13.75346614
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4083 ?
Unique (%)11.3%

Sample

1st rowHospital/ Hospice Floor
2nd rowHospital
3rd rowICU
4th rowhospital ER
5th rowon scene-alley

Common Values

ValueCountFrequency (%)
RESIDENCE2516
 
4.2%
Residence2320
 
3.8%
HOSPITAL ICU1264
 
2.1%
HOSPITAL1249
 
2.1%
ER1149
 
1.9%
SCENE1046
 
1.7%
Hospital1010
 
1.7%
HOSPITAL ER980
 
1.6%
Hospital ER692
 
1.1%
residence666
 
1.1%
Other values (5798)23099
38.2%
(Missing)24546
40.5%

Length

2022-01-11T10:46:48.988477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hospital11170
 
15.2%
residence8248
 
11.3%
4934
 
6.7%
er3986
 
5.4%
icu3727
 
5.1%
scene2939
 
4.0%
apartment1738
 
2.4%
bedroom1279
 
1.7%
inpatient1219
 
1.7%
emergency1081
 
1.5%
Other values (1746)32987
45.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

death_location_1
Categorical

HIGH CARDINALITY
MISSING

Distinct152
Distinct (%)0.3%
Missing6950
Missing (%)11.5%
Memory size3.6 MiB
Hospital
16538 
Residence
7148 
Hospital-ER
6140 
Scene
5390 
Bedroom
2450 
Other values (147)
15921 

Length

Max length24
Median length8
Mean length8.673838804
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.1%

Sample

1st rowHospital
2nd rowResidence
3rd rowHospital
4th rowHospital
5th rowHospital-ER

Common Values

ValueCountFrequency (%)
Hospital16538
27.3%
Residence7148
11.8%
Hospital-ER6140
 
10.1%
Scene5390
 
8.9%
Bedroom2450
 
4.0%
Apartment2336
 
3.9%
Emergency Room1482
 
2.4%
Nursing Home1235
 
2.0%
Hospice1233
 
2.0%
Decedent's Home1145
 
1.9%
Other values (142)8490
14.0%
(Missing)6950
11.5%

Length

2022-01-11T10:46:49.041364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hospital16538
27.6%
residence7149
11.9%
hospital-er6140
 
10.3%
scene5390
 
9.0%
home2927
 
4.9%
bedroom2450
 
4.1%
apartment2427
 
4.1%
room2358
 
3.9%
emergency1482
 
2.5%
nursing1235
 
2.1%
Other values (169)11739
19.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

covid_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
14175 
1.0
12175 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.014175
23.4%
1.012175
 
20.1%

Length

2022-01-11T10:46:49.083949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.110422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.014175
23.4%
1.012175
 
20.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

record_id
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct26350
Distinct (%)100.0%
Missing34187
Missing (%)56.5%
Memory size2.8 MiB
ME2019-04791
 
1
ME2019-04748
 
1
ME2019-04749
 
1
ME2019-04752
 
1
ME2019-04754
 
1
Other values (26345)
26345 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26350 ?
Unique (%)100.0%

Sample

1st rowME2021-11638
2nd rowME2021-11637
3rd rowME2021-11634
4th rowME2021-11633
5th rowME2021-11632

Common Values

ValueCountFrequency (%)
ME2019-047911
 
< 0.1%
ME2019-047481
 
< 0.1%
ME2019-047491
 
< 0.1%
ME2019-047521
 
< 0.1%
ME2019-047541
 
< 0.1%
ME2019-047561
 
< 0.1%
ME2019-047611
 
< 0.1%
ME2019-047621
 
< 0.1%
ME2019-047771
 
< 0.1%
ME2019-047801
 
< 0.1%
Other values (26340)26340
43.5%
(Missing)34187
56.5%

Length

2022-01-11T10:46:49.140999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
me2019-047911
 
< 0.1%
me2021-116131
 
< 0.1%
me2021-116331
 
< 0.1%
me2021-116321
 
< 0.1%
me2021-116281
 
< 0.1%
me2021-116271
 
< 0.1%
me2021-116261
 
< 0.1%
me2021-116241
 
< 0.1%
me2021-116231
 
< 0.1%
me2021-116221
 
< 0.1%
Other values (26340)26340
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

covid_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
23763 
1.0
 
2587

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.023763
39.3%
1.02587
 
4.3%

Length

2022-01-11T10:46:49.179908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.206223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.023763
39.3%
1.02587
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hypoxic-ischemic_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
24262 
1.0
 
2088

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.024262
40.1%
1.02088
 
3.4%

Length

2022-01-11T10:46:49.236850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.263351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.024262
40.1%
1.02088
 
3.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hypoxia_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
24262 
1.0
 
2088

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.024262
40.1%
1.02088
 
3.4%

Length

2022-01-11T10:46:49.294023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.320341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.024262
40.1%
1.02088
 
3.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

op-name_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
25717 
1.0
 
633

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.025717
42.5%
1.0633
 
1.0%

Length

2022-01-11T10:46:49.349618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.374670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.025717
42.5%
1.0633
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

drug_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
14922 
1.0
11428 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.014922
24.6%
1.011428
 
18.9%

Length

2022-01-11T10:46:49.404765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.517701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.014922
24.6%
1.011428
 
18.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nonfentanyl_opioid_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
20334 
1.0
6016 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.020334
33.6%
1.06016
 
9.9%

Length

2022-01-11T10:46:49.550150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.586759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.020334
33.6%
1.06016
 
9.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

opiate_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
17775 
1.0
8575 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.017775
29.4%
1.08575
 
14.2%

Length

2022-01-11T10:46:49.624623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.660306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.017775
29.4%
1.08575
 
14.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

alcohol_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
22219 
1.0
4131 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.022219
36.7%
1.04131
 
6.8%

Length

2022-01-11T10:46:49.694045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.721460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.022219
36.7%
1.04131
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

eth_alc_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
22219 
1.0
4131 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.022219
36.7%
1.04131
 
6.8%

Length

2022-01-11T10:46:49.753912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.781939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.022219
36.7%
1.04131
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hypoxic-ischemic_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26281 
1.0
 
69

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026281
43.4%
1.069
 
0.1%

Length

2022-01-11T10:46:49.814007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.840818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026281
43.4%
1.069
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hypoxia_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26281 
1.0
 
69

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026281
43.4%
1.069
 
0.1%

Length

2022-01-11T10:46:49.873372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.901674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026281
43.4%
1.069
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amphetamine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26177 
1.0
 
173

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026177
43.2%
1.0173
 
0.3%

Length

2022-01-11T10:46:49.933861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:49.961147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026177
43.2%
1.0173
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

stimulant_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
22572 
1.0
3778 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.022572
37.3%
1.03778
 
6.2%

Length

2022-01-11T10:46:49.992895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.020975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.022572
37.3%
1.03778
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

methamphetamine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26109 
1.0
 
241

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026109
43.1%
1.0241
 
0.4%

Length

2022-01-11T10:46:50.053909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.081043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026109
43.1%
1.0241
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amphetamine_based_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26215 
1.0
 
135

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026215
43.3%
1.0135
 
0.2%

Length

2022-01-11T10:46:50.112431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.139365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026215
43.3%
1.0135
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hallucinogen_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26005 
1.0
 
345

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026005
43.0%
1.0345
 
0.6%

Length

2022-01-11T10:46:50.170179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.196577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026005
43.0%
1.0345
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

benzodiazepine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
24634 
1.0
 
1716

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.024634
40.7%
1.01716
 
2.8%

Length

2022-01-11T10:46:50.226878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.254084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.024634
40.7%
1.01716
 
2.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

benzo_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
24634 
1.0
 
1716

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.024634
40.7%
1.01716
 
2.8%

Length

2022-01-11T10:46:50.285745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.313397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.024634
40.7%
1.01716
 
2.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sedative_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
24501 
1.0
 
1849

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.024501
40.5%
1.01849
 
3.1%

Length

2022-01-11T10:46:50.345759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.373679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.024501
40.5%
1.01849
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cocaine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
22922 
1.0
3428 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.022922
37.9%
1.03428
 
5.7%

Length

2022-01-11T10:46:50.406375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.434650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.022922
37.9%
1.03428
 
5.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

alcohol_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
24569 
1.0
 
1781

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.024569
40.6%
1.01781
 
2.9%

Length

2022-01-11T10:46:50.467320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.495255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.024569
40.6%
1.01781
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

drug_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
23961 
1.0
 
2389

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.023961
39.6%
1.02389
 
3.9%

Length

2022-01-11T10:46:50.527446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.555355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.023961
39.6%
1.02389
 
3.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

eth_alc_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
24569 
1.0
 
1781

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.024569
40.6%
1.01781
 
2.9%

Length

2022-01-11T10:46:50.675497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.702201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.024569
40.6%
1.01781
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fentanyl-name_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
20521 
1.0
5829 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.020521
33.9%
1.05829
 
9.6%

Length

2022-01-11T10:46:50.733399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.760877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.020521
33.9%
1.05829
 
9.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fentanyl_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
20459 
1.0
5891 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.020459
33.8%
1.05891
 
9.7%

Length

2022-01-11T10:46:50.791944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.818679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.020459
33.8%
1.05891
 
9.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lorazepam_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26217 
1.0
 
133

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026217
43.3%
1.0133
 
0.2%

Length

2022-01-11T10:46:50.849640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.875526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026217
43.3%
1.0133
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

clonazepam_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
25968 
1.0
 
382

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.025968
42.9%
1.0382
 
0.6%

Length

2022-01-11T10:46:50.906279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.932129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.025968
42.9%
1.0382
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

methadone_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
25708 
1.0
 
642

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.025708
42.5%
1.0642
 
1.1%

Length

2022-01-11T10:46:50.960992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:50.985815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.025708
42.5%
1.0642
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

anpp_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
23357 
1.0
 
2993

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.023357
38.6%
1.02993
 
4.9%

Length

2022-01-11T10:46:51.014578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.039981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.023357
38.6%
1.02993
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fen_analog_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
22541 
1.0
3809 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.022541
37.2%
1.03809
 
6.3%

Length

2022-01-11T10:46:51.069130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.094067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.022541
37.2%
1.03809
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

heroin_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
21938 
1.0
4412 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.021938
36.2%
1.04412
 
7.3%

Length

2022-01-11T10:46:51.123089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.148043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.021938
36.2%
1.04412
 
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

xylazine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26193 
1.0
 
157

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026193
43.3%
1.0157
 
0.3%

Length

2022-01-11T10:46:51.176950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.201814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026193
43.3%
1.0157
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

morphine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26185 
1.0
 
165

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026185
43.3%
1.0165
 
0.3%

Length

2022-01-11T10:46:51.231350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.256204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026185
43.3%
1.0165
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

alprazolam_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
25416 
1.0
 
934

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.025416
42.0%
1.0934
 
1.5%

Length

2022-01-11T10:46:51.285121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.309917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.025416
42.0%
1.0934
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

tramadol_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26083 
1.0
 
267

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026083
43.1%
1.0267
 
0.4%

Length

2022-01-11T10:46:51.338724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.363532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026083
43.1%
1.0267
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pcp_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26149 
1.0
 
201

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026149
43.2%
1.0201
 
0.3%

Length

2022-01-11T10:46:51.392459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.417259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026149
43.2%
1.0201
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

buprenorphine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26279 
1.0
 
71

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026279
43.4%
1.071
 
0.1%

Length

2022-01-11T10:46:51.446209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.471024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026279
43.4%
1.071
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hydromorphone_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
25914 
1.0
 
436

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.025914
42.8%
1.0436
 
0.7%

Length

2022-01-11T10:46:51.499899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.524798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.025914
42.8%
1.0436
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hydrocodone_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
25911 
1.0
 
439

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.025911
42.8%
1.0439
 
0.7%

Length

2022-01-11T10:46:51.553786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.578632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.025911
42.8%
1.0439
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

inhalant_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26135 
1.0
 
215

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026135
43.2%
1.0215
 
0.4%

Length

2022-01-11T10:46:51.607474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.723980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026135
43.2%
1.0215
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

acetylfentanyl_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
25407 
1.0
 
943

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.025407
42.0%
1.0943
 
1.6%

Length

2022-01-11T10:46:51.753111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.778283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.025407
42.0%
1.0943
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

mitragynine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26263 
1.0
 
87

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026263
43.4%
1.087
 
0.1%

Length

2022-01-11T10:46:51.808690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.835092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026263
43.4%
1.087
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

oxycodone_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26201 
1.0
 
149

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026201
43.3%
1.0149
 
0.2%

Length

2022-01-11T10:46:51.865174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.891527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026201
43.3%
1.0149
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cocaine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
25872 
1.0
 
478

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.025872
42.7%
1.0478
 
0.8%

Length

2022-01-11T10:46:51.921955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:51.948642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.025872
42.7%
1.0478
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

stimulant_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
25842 
1.0
 
508

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.025842
42.7%
1.0508
 
0.8%

Length

2022-01-11T10:46:51.979173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.005559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.025842
42.7%
1.0508
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dihydrocodeine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
25967 
1.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.025967
42.9%
1.0383
 
0.6%

Length

2022-01-11T10:46:52.036013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.061158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.025967
42.9%
1.0383
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diazepam_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26100 
1.0
 
250

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026100
43.1%
1.0250
 
0.4%

Length

2022-01-11T10:46:52.089970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.114895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026100
43.1%
1.0250
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

topiramate_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26311 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026311
43.5%
1.039
 
0.1%

Length

2022-01-11T10:46:52.143836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.169221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026311
43.5%
1.039
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cyclobenzaprine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26222 
1.0
 
128

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026222
43.3%
1.0128
 
0.2%

Length

2022-01-11T10:46:52.199802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.226466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026222
43.3%
1.0128
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

toxic_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26200 
1.0
 
150

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026200
43.3%
1.0150
 
0.2%

Length

2022-01-11T10:46:52.257658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.284494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026200
43.3%
1.0150
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

oxazepam_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26335 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026335
43.5%
1.015
 
< 0.1%

Length

2022-01-11T10:46:52.315533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.342302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026335
43.5%
1.015
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

temazepam_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26309 
1.0
 
41

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026309
43.5%
1.041
 
0.1%

Length

2022-01-11T10:46:52.373315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.399998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026309
43.5%
1.041
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

carfentanyl_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26306 
1.0
 
44

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026306
43.5%
1.044
 
0.1%

Length

2022-01-11T10:46:52.430635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.457080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026306
43.5%
1.044
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ketamine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26338 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026338
43.5%
1.012
 
< 0.1%

Length

2022-01-11T10:46:52.486342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.511163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026338
43.5%
1.012
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

oxymorphone_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26318 
1.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026318
43.5%
1.032
 
0.1%

Length

2022-01-11T10:46:52.539809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.564638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026318
43.5%
1.032
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

codeine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26299 
1.0
 
51

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026299
43.4%
1.051
 
0.1%

Length

2022-01-11T10:46:52.594417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.621119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026299
43.4%
1.051
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

methocarbamol_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26344 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026344
43.5%
1.06
 
< 0.1%

Length

2022-01-11T10:46:52.651523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.678269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026344
43.5%
1.06
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

chlordiazepoxide_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26329 
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026329
43.5%
1.021
 
< 0.1%

Length

2022-01-11T10:46:52.799699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.825331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026329
43.5%
1.021
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

acetylsalicylic acid_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26341 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026341
43.5%
1.09
 
< 0.1%

Length

2022-01-11T10:46:52.855700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.882002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026341
43.5%
1.09
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

acetylsalicylic acid_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26331 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026331
43.5%
1.019
 
< 0.1%

Length

2022-01-11T10:46:52.912290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.939082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026331
43.5%
1.019
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

polysubstance_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26348 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Length

2022-01-11T10:46:52.968686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:52.993880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

levomethorphan_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26307 
1.0
 
43

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026307
43.5%
1.043
 
0.1%

Length

2022-01-11T10:46:53.022502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.047514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026307
43.5%
1.043
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

levorphanol_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26317 
1.0
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026317
43.5%
1.033
 
0.1%

Length

2022-01-11T10:46:53.076310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.101202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026317
43.5%
1.033
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

butyryl_fentanyl_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26347 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026347
43.5%
1.03
 
< 0.1%

Length

2022-01-11T10:46:53.129986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.155225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026347
43.5%
1.03
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

heroin_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26268 
1.0
 
82

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026268
43.4%
1.082
 
0.1%

Length

2022-01-11T10:46:53.184928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.210940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026268
43.4%
1.082
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nonfentanyl_opioid_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26205 
1.0
 
145

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026205
43.3%
1.0145
 
0.2%

Length

2022-01-11T10:46:53.241766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.268545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026205
43.3%
1.0145
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

opiate_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26176 
1.0
 
174

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026176
43.2%
1.0174
 
0.3%

Length

2022-01-11T10:46:53.299400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.326169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026176
43.2%
1.0174
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fentanyl-name_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26277 
1.0
 
73

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026277
43.4%
1.073
 
0.1%

Length

2022-01-11T10:46:53.356645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.383406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026277
43.4%
1.073
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fentanyl_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26276 
1.0
 
74

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026276
43.4%
1.074
 
0.1%

Length

2022-01-11T10:46:53.413781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.440116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026276
43.4%
1.074
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

anpp_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26330 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026330
43.5%
1.020
 
< 0.1%

Length

2022-01-11T10:46:53.470927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.497638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026330
43.5%
1.020
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fen_analog_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26321 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026321
43.5%
1.029
 
< 0.1%

Length

2022-01-11T10:46:53.527894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.554717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026321
43.5%
1.029
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

valerylfentanyl_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26335 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026335
43.5%
1.015
 
< 0.1%

Length

2022-01-11T10:46:53.585345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.611737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026335
43.5%
1.015
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

estazolam_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26348 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Length

2022-01-11T10:46:53.642636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.669072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

carisoprodol_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26337 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026337
43.5%
1.013
 
< 0.1%

Length

2022-01-11T10:46:53.699560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.726375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026337
43.5%
1.013
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

inhalant_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26335 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026335
43.5%
1.015
 
< 0.1%

Length

2022-01-11T10:46:53.757269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.874988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026335
43.5%
1.015
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lsd_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26346 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Length

2022-01-11T10:46:53.903999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.929619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

propoxyphene_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:53.959436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:53.985740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fbf_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26331 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026331
43.5%
1.019
 
< 0.1%

Length

2022-01-11T10:46:54.015357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.041367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026331
43.5%
1.019
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

barbiturates_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26346 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Length

2022-01-11T10:46:54.071812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.098176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amphetamine_based_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26344 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026344
43.5%
1.06
 
< 0.1%

Length

2022-01-11T10:46:54.128995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.155482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026344
43.5%
1.06
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hallucinogen_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26319 
1.0
 
31

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026319
43.5%
1.031
 
0.1%

Length

2022-01-11T10:46:54.186277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.212966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026319
43.5%
1.031
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

marijuana_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26342 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Length

2022-01-11T10:46:54.243534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.270325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cannabis_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26342 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Length

2022-01-11T10:46:54.300500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.327282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

op-name_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26320 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026320
43.5%
1.030
 
< 0.1%

Length

2022-01-11T10:46:54.358255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.385085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026320
43.5%
1.030
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

benzodiazepine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26321 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026321
43.5%
1.029
 
< 0.1%

Length

2022-01-11T10:46:54.416002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.441778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026321
43.5%
1.029
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

benzo_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26321 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026321
43.5%
1.029
 
< 0.1%

Length

2022-01-11T10:46:54.472681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.500247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026321
43.5%
1.029
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sedative_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26319 
1.0
 
31

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026319
43.5%
1.031
 
0.1%

Length

2022-01-11T10:46:54.531993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.560235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026319
43.5%
1.031
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

methylphenidate_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26346 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Length

2022-01-11T10:46:54.592902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.620938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

u-47700_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26308 
1.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026308
43.5%
1.042
 
0.1%

Length

2022-01-11T10:46:54.653372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.681651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026308
43.5%
1.042
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

norfentanyl_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26328 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026328
43.5%
1.022
 
< 0.1%

Length

2022-01-11T10:46:54.714015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.742248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026328
43.5%
1.022
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pcp_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26328 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026328
43.5%
1.022
 
< 0.1%

Length

2022-01-11T10:46:54.774469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.802246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026328
43.5%
1.022
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

polysubstance_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26339 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026339
43.5%
1.011
 
< 0.1%

Length

2022-01-11T10:46:54.834270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:54.861931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026339
43.5%
1.011
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

metaxalone_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26345 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026345
43.5%
1.05
 
< 0.1%

Length

2022-01-11T10:46:54.894173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.015760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026345
43.5%
1.05
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

tarpentadol_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:55.045651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.072329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

methadone_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26325 
1.0
 
25

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026325
43.5%
1.025
 
< 0.1%

Length

2022-01-11T10:46:55.103976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.131337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026325
43.5%
1.025
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cyclopropyl_fentanyl_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26204 
1.0
 
146

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026204
43.3%
1.0146
 
0.2%

Length

2022-01-11T10:46:55.163554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.191640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026204
43.3%
1.0146
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

phentermine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26346 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Length

2022-01-11T10:46:55.223305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.250041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

methoxyacetyl_fentanyl_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26312 
1.0
 
38

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026312
43.5%
1.038
 
0.1%

Length

2022-01-11T10:46:55.278905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.303777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026312
43.5%
1.038
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

furanyl_fentanyl_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26135 
1.0
 
215

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026135
43.2%
1.0215
 
0.4%

Length

2022-01-11T10:46:55.332453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.357311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026135
43.2%
1.0215
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fbf_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:55.386202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.411057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fibf_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26346 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Length

2022-01-11T10:46:55.439757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.464660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

flurazepam_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26345 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026345
43.5%
1.05
 
< 0.1%

Length

2022-01-11T10:46:55.493389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.519420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026345
43.5%
1.05
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

acetylfentanyl_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26341 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026341
43.5%
1.09
 
< 0.1%

Length

2022-01-11T10:46:55.549809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.576400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026341
43.5%
1.09
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

u-49900_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26347 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026347
43.5%
1.03
 
< 0.1%

Length

2022-01-11T10:46:55.605452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.630290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026347
43.5%
1.03
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

tizanidine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:55.659051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.683915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

furanyl_fentanyl_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:55.712571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.737431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lsd_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:55.765980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.790874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

methamphetamine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26326 
1.0
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026326
43.5%
1.024
 
< 0.1%

Length

2022-01-11T10:46:55.820604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.847366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026326
43.5%
1.024
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hydromorphone_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26342 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Length

2022-01-11T10:46:55.878185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.904976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hydrocodone_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26342 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Length

2022-01-11T10:46:55.935719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:55.962893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dihydrocodeine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26342 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Length

2022-01-11T10:46:55.994224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.117106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amphetamine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26337 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026337
43.5%
1.013
 
< 0.1%

Length

2022-01-11T10:46:56.146791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.173108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026337
43.5%
1.013
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

meperidine_primary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:56.203463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.230161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

clonazepam_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26344 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026344
43.5%
1.06
 
< 0.1%

Length

2022-01-11T10:46:56.260600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.287325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026344
43.5%
1.06
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

alprazolam_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26336 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026336
43.5%
1.014
 
< 0.1%

Length

2022-01-11T10:46:56.318035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.344837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026336
43.5%
1.014
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

xylazine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26348 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Length

2022-01-11T10:46:56.375703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.402410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

mitragynine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26348 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Length

2022-01-11T10:46:56.433583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.460327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

codeine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26346 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Length

2022-01-11T10:46:56.491269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.516882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026346
43.5%
1.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ketamine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26348 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Length

2022-01-11T10:46:56.545671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.570527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diazepam_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26348 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Length

2022-01-11T10:46:56.599290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.624196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

oxycodone_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26342 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Length

2022-01-11T10:46:56.652936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.677801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026342
43.5%
1.08
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

levomethorphan_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:56.706438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.731453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

levorphanol_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:56.760033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.784858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lorazepam_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:56.813357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.838241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

marijuana_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26348 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Length

2022-01-11T10:46:56.867444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.893928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cannabis_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26348 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Length

2022-01-11T10:46:56.924187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:56.950922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

oxymorphone_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:56.980863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:57.007607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

tramadol_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26348 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Length

2022-01-11T10:46:57.038198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:57.064892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026348
43.5%
1.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cyclobenzaprine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:57.095140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:57.216832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

toxic_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:57.245756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:57.271631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

topiramate_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:57.301526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:57.328148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

morphine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26344 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026344
43.5%
1.06
 
< 0.1%

Length

2022-01-11T10:46:57.358795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:57.385153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026344
43.5%
1.06
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

buprenorphine_secondary
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
9.0
34187 
0.0
26349 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Length

2022-01-11T10:46:57.416063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T10:46:57.442810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.034187
56.5%
0.026349
43.5%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-01-11T10:46:34.436803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:05.148473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:06.428817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:07.756890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:09.040622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:10.235637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:11.456026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:12.757400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:14.068245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:15.361114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:16.606764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:17.890866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:19.212166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:20.530442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:21.836273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:23.150083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:24.445237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:25.698540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:26.909513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:28.287112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:29.335746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:30.505977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:31.848184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:33.096354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:34.488226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:05.227777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:06.480301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:07.806913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:09.084592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:10.284386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:11.505693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:12.807143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:14.116448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:15.411075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:16.655032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:17.941600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:19.262177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:20.581799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:21.971019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:23.200776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:24.493583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:25.746516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:27.043156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:28.326539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:29.382698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:30.555855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:31.896915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:33.146999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:34.544353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:05.287096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:06.535015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:07.859140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:09.119236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:10.340122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:11.563621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:12.859883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:14.172596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:15.464840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:16.709086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:17.995450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:19.315982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:20.633447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:22.022553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:23.252788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:24.546897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:25.800669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:27.097223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:28.369537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:29.430685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:30.609266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:31.951958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:33.204304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:34.593204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:05.348138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-01-11T10:46:28.027225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:29.125405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:30.266696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:31.497793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:32.843605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:34.176248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:35.534422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:06.224093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:07.536578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:08.841800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:10.046660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:11.246695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:12.559169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:13.866172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:15.161478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:16.401644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:17.687498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:19.003699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:20.321256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:21.607231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:22.947145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:24.239059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:25.504805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:26.703030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:28.083232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:29.167985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:30.315659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:31.549573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:32.894949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:34.229804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:35.590304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:06.279084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:07.596912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:08.892808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:10.095086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:11.300997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:12.609974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:13.917396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:15.211585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:16.454618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:17.739847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:19.057484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:20.372903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:21.664008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:22.997894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:24.290767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:25.553998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:26.756369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:28.136531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:29.210968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:30.365136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:31.603822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:32.945349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:34.284745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:35.643292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:06.328713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:07.652487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:08.941705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:10.139860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:11.354301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:12.658249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:13.966659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:15.260809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:16.504681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:17.791726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:19.109170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:20.425576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:21.720361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:23.047158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:24.342063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:25.601144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:26.808619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:28.188194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:29.251221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:30.411268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:31.658390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:32.994530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:34.334177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:35.695666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:06.379575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:07.705802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:08.993815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:10.185116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:11.404844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:12.708244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:14.017716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:15.310438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:16.555674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:17.841757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:19.161210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:20.479680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:21.778844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:23.098919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:24.394751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:25.649755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:26.859957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:28.240794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:29.294035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:30.458519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:31.714523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:33.044315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-11T10:46:34.384566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-01-11T10:46:57.624265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-11T10:46:58.966348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-11T10:47:00.375750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-11T10:47:01.691123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-11T10:46:37.402592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-11T10:46:40.795985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-11T10:46:42.006328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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0ME2021-117232021-12-13T16:00:00.0002021-12-1466.0FemaleWhite00010.0Chicago60659.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN041.984573-87.694033-1.0000009801.38487831.01130.0NaNNaNNaNNaN17.031.020801.01.703102e+10208.01Census Tract 208.01G5020S655953.00.041.986808-87.694415NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMulti-FamilyResidentialUrbanized2021-12-14 20:15:0020:15:002021.012.014.050.0NaN00NaN00012/13/2021 04:00 PM12/14/2021 08:15 PM5145 North California AvenueChicagoCook CountyIL60659Hospital/ Hospice FloorHospital9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
1ME2021-117222021-12-15T02:55:00.0002021-12-1567.0FemaleBlack0001.0Chicago60644.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN041.886646-87.7592679816.4968239801.38487831.01111.0NaNNaNNaNNaN17.031.0251900.01.703125e+102519.00Census Tract 2519G5020S632452.00.041.883812-87.760094NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSingle-Family DetachedResidentialUrbanized2021-12-15 03:16:0003:16:002021.012.015.050.0NaN00NaN01112/15/2021 02:55 AM12/15/2021 03:16 AM334 N Lorel Ave., 2nd FloorChicagoNaNIL60644NaNResidence9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
2ME2021-117212021-12-07T09:38:00.0002021-12-1580.0FemaleWhite00010.0Chicago60646.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN041.986009-87.781317-1.0000009801.38487831.01111.0NaNNaNNaNNaN17.031.0100100.01.703110e+101001.00Census Tract 1001G5020S1182384.00.041.989584-87.783014NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSingle-Family DetachedResidentialUrbanized2021-12-15 00:29:0000:29:002021.012.015.050.0NaN00NaN01112/07/2021 09:38 AM12/15/2021 12:29 AM7435 West Talcott AvenueChicagoCook CountyIL60631HospitalHospital9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
3ME2021-117202021-12-14T20:00:00.0002021-12-15NaNFemaleWhite000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN92.0Tri State Toll Rd, Hinsdale, Illinois, 605215800 tri state toll rd 5801 HINSDALE 60521141.772205-87.9072059821.6018681.59641031.06000.0NaNNaNNaNNaNNaN17.031.0820101.01.703182e+108201.01Census Tract 8201.01G5020S6973465.048267.041.776190-87.903427NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNON-PARCEL AREASNon-Parcel AreasNon-Parcel Areas2021-12-15 00:52:0000:52:002021.012.015.050.00.000NaN00012/14/2021 08:00 PM12/15/2021 12:52 AM2160 South 1st AvenueMaywoodCook CountyILNaNICUHospital9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
4ME2021-117192021-12-15T00:01:00.0002021-12-1536.0FemaleBlack0006.0Chicago Ridge60415.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN041.711734-87.794728-1.0000009801.38487831.01130.0NaNNaNNaNNaN17.031.0823001.01.703182e+108230.01Census Tract 8230.01G5020S3644304.00.041.706965-87.784187NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMulti-FamilyResidentialUrbanized2021-12-15 00:40:0000:40:002021.012.015.050.0NaN00NaN01112/15/2021 12:01 AM12/15/2021 12:40 AMAdvocate Christ Medical Center 4440 W 95th St.Oak LawnCook CountyIL60453hospital ERHospital-ER9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
5ME2021-117182021-12-14T18:05:00.0002021-12-1461.0MaleBlack0003.0Chicago60653.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN041.751085-87.6026789823.7124379801.38487831.06000.0NaNNaNNaNNaNNaN17.031.0440101.01.703144e+104401.01Census Tract 4401.01G5020S377008.00.041.748627-87.601451NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNON-PARCEL AREASNon-Parcel AreasNon-Parcel Areas2021-12-14 18:10:0018:10:002021.012.014.050.0NaN00NaN01112/14/2021 06:05 PM12/14/2021 06:10 PM7911 S Drexel AveChicagoNaNIL60619on scene-alleyAlley9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
6ME2021-117172021-12-14T16:00:00.0002021-12-1443.0MaleWhite1007.0CHICAGO60629.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN041.776729-87.715648-1.0000009801.38487831.01111.0NaNNaNNaNNaN17.031.0650302.01.703165e+106503.02Census Tract 6503.02G5020S664019.00.041.775093-87.717733NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSingle-Family DetachedResidentialUrbanized2021-12-14 21:48:0021:48:002021.012.014.050.0NaN00NaN01112/14/2021 04:00 PM12/14/2021 09:48 PM4440 West 95th StreetOak LawnCook CountyIL60453HospitalHospital9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
7ME2021-117162021-12-14T21:56:00.0002021-12-1426.0MaleWhite10011.0Hometown60456.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN041.730161-87.7363839833.5338109801.38487831.01112.0NaNNaNNaNNaN17.031.0822000.01.703182e+108220.00Census Tract 8220G5020S1240338.00.041.731248-87.731134NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSingle-Family AttachedResidentialUrbanized2021-12-14 22:35:0022:35:002021.012.014.050.0NaN00NaN01112/14/2021 09:56 PM12/14/2021 10:35 PM4331 Southwest HighwayOak LawnCook CountyIL60453Hospital ERHospital-ER9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
8ME2021-117152021-12-14T21:16:00.0002021-12-1446.0MaleBlack0005.0Chicago60628.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN041.676049-87.631701-1.0000009801.38487831.06000.0NaNNaNNaNNaNNaN17.031.0530501.01.703153e+105305.01Census Tract 5305.01G5020S1296493.00.041.674278-87.632166NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNON-PARCEL AREASNon-Parcel AreasNon-Parcel Areas2021-12-14 21:50:0021:50:002021.012.014.050.0NaN00NaN01112/14/2021 09:16 PM12/14/2021 09:50 PM355 W. 120th St.ChicagoCook CountyIL60628SceneScene9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
9ME2021-117142021-12-14T00:00:00.0002021-12-1489.0FemaleBlack000NaNPhoenix60426.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaN9824.0372019801.384878NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2021-12-14 18:40:0018:40:002021.012.014.050.0NaN00NaN01112/14/2021 12:00 AM12/14/2021 06:40 PM1 INGALLS DRIVE - ICUHarveyCook CountyIL60426IN HOSPITAL-ICUNaN9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0

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60527ME2014-000102014-08-11T14:30:00.0002014-08-1150.0MaleWhite0009.0Chicago60631.0NATURALARTERIOSCLEROTIC CARDIOVASCULAR DISEASE0.00.0NaNNaNNaNNaNNaNNaNNaN041.993549-87.7996479805.0811079801.38487831.01130.0NaNNaNNaNNaN17.031.0100200.01.703110e+101002.00Census Tract 1002G5020S2060321.00.041.995773-87.794259NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMulti-FamilyResidentialUrbanized2014-08-11 15:41:0015:41:002014.08.011.033.0NaN00ARTERIOSCLEROTIC CARDIOVASCULAR DISEASE01108/11/2014 02:30 PM08/11/2014 03:41 PM6221 north NiagaraChicagoNaNIL60631NaNNaN9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
60528ME2014-000092014-06-17T18:51:00.0002014-06-2460.0MaleWhite000NaNElgin60120.0ACCIDENTCOMPLICATIONS OF BLUNT ABDOMINAL TRAUMA0.00.0METASTATIC PANCREATIC CARCINOMA.MOTOR VEHICLE STRIKING FIXED OBJECTNaNNaN100.0060120, Elgin, IllinoisELGIN 60120142.027360-88.255630-1.0000000.07698431.01140.0NaNNaNNaNNaN17.031.0804406.01.703180e+108044.06Census Tract 8044.06G5020S7052082.041081.042.017477-88.256180NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMobile Home Parks and Trailer CourtsResidentialUrbanized2014-06-24 15:41:0015:41:002014.06.024.026.00.000COMPLICATIONS OF BLUNT ABDOMINAL TRAUMAMOTOR VEHICLE STRIKING FIXED OBJECT00106/17/2014 06:51 PM06/24/2014 03:41 PM410 Lawrence AvenueElginKane CountyIL60120NaNNaN9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
60529ME2014-000082014-08-09T00:05:00.0002014-08-0974.0FemaleBlack00010.0Chicago60613.0NATURALHYPERTENSIVE ARTERIOSCLEROTIC CARDIOVASCULAR DISEASE0.00.0NaNNaNNaNNaNNaNNaNNaN041.950017-87.6464629810.8675339801.38487831.01130.0NaNNaNNaNNaN17.031.060900.01.703106e+10609.00Census Tract 609G5020S499066.0404435.041.951517-87.641778NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMulti-FamilyResidentialUrbanized2014-08-09 02:00:0002:00:002014.08.09.032.0NaN00HYPERTENSIVE ARTERIOSCLEROTIC CARDIOVASCULAR DISEASE01108/09/2014 12:05 AM08/09/2014 02:00 AM648 West WavelandChicagoCook CountyIL60613NaNNaN9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
60530ME2014-000072014-08-11T18:27:00.0002014-08-1175.0FemaleWhite0003.0Chicago60615.0SUICIDEDROWNING0.00.0NaNMULTIPLE BLUNT FORCE INJURIESJUMPED INTO LAKENaNNaNNaNNaN041.795550-87.580807-1.0000009801.38487831.03100.0MUNINaNNaNNaN17.031.0410900.01.703141e+104109.00Census Tract 4109G5020S387624.0578244.041.796710-87.578094NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNOpen Space - Primarily RecreationPrimarily RecreationOpen Space2014-08-11 14:52:0014:52:002014.08.011.033.0NaN00DROWNINGMULTIPLE BLUNT FORCE INJURIESJUMPED INTO LAKE01108/11/2014 06:27 PM08/11/2014 02:52 PM5491 South Shore DriveChicagoCook CountyIL60605NaNLake9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
60531ME2014-000062014-08-10T07:15:00.0002014-08-1082.0FemaleWhite00012.0Chicago60634.0NATURALARTERIOSCLEROTIC CARDIOVASCULAR DISEASE0.00.0NaNNaNNaNNaNNaNNaNNaN041.954293-87.7691689819.7103379801.38487831.01111.0NaNNaN17.031.0150402.01.703115e+101504.02Census Tract 1504.02G5020S662508.00.041.956798-87.772044NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSingle-Family DetachedResidentialUrbanized2014-08-10 08:00:0008:00:002014.08.010.032.0NaN00ARTERIOSCLEROTIC CARDIOVASCULAR DISEASE01108/10/2014 07:15 AM08/10/2014 08:00 AM4037 North MajorChicagoNaNIL60634NaNNaN9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
60532ME2014-000052014-08-11T11:40:00.0002014-08-1147.0MaleWhite00016.0Cicero60804.0ACCIDENTASPHYXIA0.00.0NaNCHOKING ON FOOD BOLUSNaNNaNNaNNaNNaN041.836066-87.764219-1.0000009801.38487831.01215.0NaNNaNNaNNaN17.031.0814400.01.703181e+108144.00Census Tract 8144G5020S1665055.00.041.827686-87.763547NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNUrban MixCommercialUrbanized2014-08-11 12:26:0012:26:002014.08.011.033.0NaN00ASPHYXIACHOKING ON FOOD BOLUS01108/11/2014 11:40 AM08/11/2014 12:26 PM3100 S. Central AvenueCiceroNaNIL60804NaNHospital-ER9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
60533ME2014-000042014-08-11T10:18:00.0002014-08-1124.0MaleWhite000NaNChicago60625.0NATURALPNEUMONIA0.00.0NaNNaNNaNNaN95.874620 N Spaulding Ave, Chicago, Illinois, 606254620 n. spaudling CHICAGO 60625141.965311-87.7109989826.8704400.37377531.01130.0NaNNaNNaNNaN17.031.0140701.01.703114e+101407.01Census Tract 1407.01G5020S228328.00.041.967038-87.712652NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMulti-FamilyResidentialUrbanized2014-08-11 10:35:0010:35:002014.08.011.033.00.000PNEUMONIA00008/11/2014 10:18 AM08/11/2014 10:35 AM4620 N. Spaudling 2nd Flr.ChicagoCook CountyIL60625NaNNaN9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
60534ME2014-000032014-08-11T09:05:00.0002014-08-1174.0MaleBlack0003.0Chicago60653.0NATURALARTERIOSCLEROTIC CARDIOVASCULAR DISEASE0.00.0NaNNaNNaNNaNNaNNaNNaN041.809491-87.613025-1.0000009801.38487831.06000.0NaNNaNNaNNaNNaN17.031.0381200.01.703138e+103812.00Census Tract 3812G5020S258553.00.041.808511-87.613999NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNON-PARCEL AREASNon-Parcel AreasNon-Parcel Areas2014-08-11 09:40:0009:40:002014.08.011.033.0NaN00ARTERIOSCLEROTIC CARDIOVASCULAR DISEASE01108/11/2014 09:05 AM08/11/2014 09:40 AM520 E. 47th Street Room 325ChicagoNaNIL60653NaNDecedent's Home9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
60535ME2014-00002NaN2014-08-1157.0MaleWhite000NaNChicago60614.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaN9811.9443139801.384878NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2014-08-11 13:00:0013:00:002014.08.011.033.0NaN00NaN011NaN08/11/2014 01:00 PMNaNNaNNaNNaNNaNresidenceNaN9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0
60536ME2014-000012014-08-11T06:45:00.0002014-08-1164.0MaleWhite0004.0Chicago60617.0SUICIDEGUNSHOT WOUND OF THE HEAD1.00.0NaNNaNNaNNaNNaNNaNNaN041.730320-87.548255-1.0000009801.38487831.01216.0NaNNaNNaNNaN17.031.0833900.01.703183e+108339.00Census Tract 8339G5020S677853.024124.041.731032-87.547423NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNUrban Mix w/Residential ComponentCommercialUrbanized2014-08-11 06:56:0006:56:002014.08.011.033.0NaN00GUNSHOT WOUND OF THE HEAD01108/11/2014 06:45 AM08/11/2014 06:56 AM3102 E. 91st StreetChicagoNaNIL60617NaNDecedent's Home9.0NaN9.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.09.0